user of Knlgmngr

description::
* author,
* reader,

name::
* McsEngl.Knlgmngr'user,

knowledge-language of Knlgmngr

description::
· the-knowledge-language it uses to create its knowledge-base.

name::
* McsEngl.Knlgmngr'knowledge-language,

knowledge-base of Knlgmngr

description::
· the-set of output of implementing a-knowledge-language on human-knowledge.

name::
* McsEngl.Knlgbase,
* McsEngl.Knlgmngr'knowledge-base!⇒Knlgbase,
* McsEngl.knowledge-base-of-Knlgmngr!⇒Knlgbase,

hard-sys of Knlgmngr

description::
· computer or network used.

name::
* McsEngl.Knlgmngr'hard-sys,

soft-sys of Knlgmngr

description::
· any app of the-manager.

name::
* McsEngl.Knlgmngr'soft-sys,
* McsEngl.Knlgmngr'tool,

DOING of Knlgmngr

description::
* building-Knlgbase,
* retrieving-Knlgbase,
===
* building,
* computing,
* consistency-checking,
* evoluting-of-Knlgbase,
* formating,
* generic-specific-relations-management,
* inferencing,
* integrating,
* name-management,
* navigating,
* parent-child-relations-management,
* presenting,
* reasoning,
* retrieving,
* questioning,
* storing,
* translating,
* validating,
* whole-part-relations-management,

name::
* McsEngl.Klmrdoing!=doing-of--knowledge-manager,
* McsEngl.Knlgmngr'doing!⇒Klmrdoing,

evolution of Knlgmngr

description::
"evolution of knowledge-reasoning:
The evolution of knowledge reasoning can be traced back to the earliest days of artificial intelligence (AI). In the 1950s, researchers began developing systems that could use knowledge to solve problems. One of the earliest examples of this is the General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon. GPS was able to solve a variety of problems by using a set of general problem-solving rules.

In the 1960s and 1970s, researchers began to develop more sophisticated knowledge representation systems. These systems allowed for the representation of knowledge about the world in a way that could be used by AI systems to reason about problems. One of the most important developments in this area was the development of first-order logic. First-order logic provides a formal language for representing and reasoning about knowledge.

In the 1980s and 1990s, researchers began to develop knowledge reasoning systems that could be used to solve real-world problems. One of the most successful examples of this is the MYCIN system, which was developed to diagnose infectious diseases. MYCIN used a knowledge base of medical knowledge to reason about patient symptoms and make diagnoses.

In recent years, there has been a renewed interest in knowledge reasoning due to the rise of large language models (LLMs). LLMs are able to process and understand large amounts of text data, and they can be used to build knowledge graphs that represent knowledge about the world in a structured way. This has led to the development of new knowledge reasoning systems that are able to solve more complex problems than ever before.

Here are some of the key milestones in the evolution of knowledge reasoning:
* **1959:** General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon.
* **1960s:** Development of first-order logic as a formal language for representing and reasoning about knowledge.
* **1970s:** Development of knowledge representation systems such as KL-ONE and PROLOG.
* **1980s:** Development of MYCIN system for diagnosing infectious diseases.
* **1990s:** Development of knowledge reasoning systems for real-world applications such as fraud detection and customer relationship management.
* **2000s:** Rise of large language models and the development of new knowledge reasoning systems that are able to solve more complex problems.

The future of knowledge reasoning is bright. As knowledge graphs become more comprehensive and machine learning algorithms become more sophisticated, knowledge reasoning systems will become increasingly powerful and useful. Knowledge reasoning will play a vital role in many different areas of our lives, including healthcare, education, and business.

Here are some specific examples of how knowledge reasoning is being used today:
* **Medical diagnosis:** Knowledge reasoning systems are being used to help doctors diagnose diseases more accurately.
* **Fraud detection:** Knowledge reasoning systems are being used to detect fraudulent transactions in financial systems.
* **Customer service:** Knowledge reasoning systems are being used to provide better customer service by answering questions and resolving issues more quickly.
* **Product recommendation:** Knowledge reasoning systems are being used to recommend products to customers based on their past purchases and preferences.

These are just a few examples of how knowledge reasoning is being used today. As knowledge reasoning systems continue to evolve, they will be used to solve even more complex problems in the future."
[{2023-10-26 retrieved} https://bard.google.com/chat/cecdcc24288caf0b]

"evolution of knowledge-reasoning-systems:
The evolution of knowledge reasoning systems can be traced through the development of artificial intelligence (AI) and knowledge representation techniques. These systems have undergone significant changes and improvements over the years. Here is an overview of their evolution:

1. Early Expert Systems (1960s-1970s):
- The field of knowledge reasoning systems began with early expert systems, which were rule-based systems designed to emulate human expertise in specific domains.
- Dendral (1965) and MYCIN (1976) are notable examples of early expert systems.
- These systems used a "knowledge base" of rules and an "inference engine" to make decisions based on the available knowledge.

2. Knowledge Representation (1970s-1980s):
- During this period, researchers focused on developing formal knowledge representation languages and ontologies to capture knowledge in a structured manner.
- The development of languages like Prolog and semantic networks played a significant role in this evolution.

3. Frame-Based Systems (1980s-1990s):
- Frame-based systems, such as the KL-ONE system, introduced a way to represent and reason with structured knowledge using frames and slots.
- These systems enabled more complex reasoning about concepts and relationships.

4. Rule-Based Expert Systems (1980s-1990s):
- Rule-based expert systems continued to advance, incorporating more sophisticated reasoning mechanisms.
- The introduction of uncertainty and fuzzy logic allowed these systems to deal with incomplete or uncertain information.

5. Semantic Web and Ontologies (late 1990s-present):
- The Semantic Web initiative aimed to create a web of structured and interconnected data using ontologies and the Resource Description Framework (RDF).
- The development of ontologies like OWL (Web Ontology Language) made it possible to reason about complex relationships on the web.

6. Knowledge Graphs (2000s-present):
- Knowledge graphs have gained popularity as a way to represent and reason with interconnected data.
- Large-scale knowledge graphs, like Google's Knowledge Graph and the Wikidata project, have become valuable resources for various applications, including search engines and virtual assistants.

7. Machine Learning and Neural Networks (2010s-present):
- Deep learning and neural networks have made significant contributions to knowledge reasoning systems.
- Neural networks are used for various tasks, including natural language understanding, question answering, and image recognition, allowing systems to reason and make inferences from unstructured data.

8. Hybrid Approaches (2010s-present):
- Contemporary knowledge reasoning systems often combine symbolic reasoning and machine learning techniques.
- This hybrid approach enables systems to leverage structured knowledge along with the ability to learn from data.

9. Explainability and Trust (ongoing):
- As knowledge reasoning systems become more complex and integrated into various applications, there is a growing emphasis on explainability and trustworthiness in AI systems.
- Researchers are developing techniques to make AI systems more transparent and interpretable, especially in critical domains.

The evolution of knowledge reasoning systems reflects the broader trends in AI and computer science, moving from early rule-based expert systems to more flexible, data-driven, and interconnected knowledge representations. The field continues to advance, driven by the need for intelligent systems that can make informed decisions, understand natural language, and adapt to changing environments."
[{2023-10-26 retrieved} https://chat.openai.com/c/44a972fd-ca81-4068-b372-53a32715dc07]

name::
* McsEngl.evoluting-of-Knlgmngr,
* McsEngl.Knlgmngr'evoluting,

GENERIC-SPECIFIC-TREE of Knlgmngr

generic-tree-of-Knlgmngr::
* hard-soft-system,
* ... entity,
* McsEngl.Knlgmngr'generic-tree,

specific-tree-of-Knlgmngr::
* concept-Knlgmngr,
* logo-Knlgmngr,
===
* AAj,
* Algernon,
* MYCIN,

* McsEngl.Knlgmngr.specific-tree,

Knlgmngr.concept (link)

Knlgmngr.logo (link)

Knlgmngr.LLM-Large-Language-Model (link)

Knlgmngr.logic (link)

Knlgmngr.DSS-Decision-Support-System

description::
"overview of DSS:
A decision support system (DSS) is a computer-based information system that supports decision-making activities in semi-structured or unstructured tasks. DSSs provide users with access to data, models, and tools that can help them to identify and evaluate alternative solutions to a problem. DSSs are typically used by managers and decision-makers at all levels of an organization.

DSSs can be classified into several different types, including:
* **Data-driven DSSs:** Data-driven DSSs focus on providing users with access to and the ability to analyze large amounts of data.
* **Model-driven DSSs:** Model-driven DSSs use mathematical models to represent and analyze complex problems.
* **Knowledge-driven DSSs:** Knowledge-driven DSSs use expert knowledge to solve complex problems.
* **Document-driven DSSs:** Document-driven DSSs provide users with access to and the ability to analyze documents, such as reports, presentations, and emails.
* **Communications-driven DSSs:** Communications-driven DSSs facilitate communication and collaboration among decision-makers.

DSSs are used in a wide variety of applications, including:
* **Sales and marketing:** DSSs can be used to forecast sales, plan marketing campaigns, and segment customers.
* **Operations management:** DSSs can be used to schedule production, manage inventory, and optimize supply chains.
* **Financial management:** DSSs can be used to budget, forecast financial performance, and make investment decisions.
* **Human resource management:** DSSs can be used to recruit and select employees, train and develop employees, and manage performance.
* **Research and development:** DSSs can be used to identify and evaluate research projects, develop new products and services, and manage intellectual property.

**Benefits of DSS**
DSSs offer a number of benefits, including:
* **Improved decision-making:** DSSs can help decision-makers to make better decisions by providing them with access to data, models, and tools that can help them to identify and evaluate alternative solutions.
* **Increased efficiency and productivity:** DSSs can help decision-makers to save time and improve their productivity by automating tasks and providing them with the information they need to make decisions quickly and accurately.
* **Reduced costs:** DSSs can help organizations to reduce costs by improving decision-making and increasing efficiency.
* **Enhanced communication and collaboration:** DSSs can facilitate communication and collaboration among decision-makers, which can lead to better decision-making and improved outcomes.

**Challenges of DSS**
DSSs also face some challenges, including:
* **Complexity:** DSSs can be complex to develop and implement.
* **Data quality:** DSSs rely on high-quality data to produce accurate results.
* **User acceptance:** DSSs can be difficult to use if they are not properly designed and implemented.

**Conclusion**
DSSs are powerful tools that can help organizations to improve decision-making, increase efficiency and productivity, reduce costs, and enhance communication and collaboration. DSSs are used in a wide variety of applications, and they are becoming increasingly important as organizations face more complex and challenging decision-making environments."
[{2023-10-28 retrieved} https://bard.google.com/chat/5b4a3137667cf549]

name::
* McsEngl.DSS!=decision-support-system--Knlgmngr,
* McsEngl.Knlgmngr.020-DSS,
* McsEngl.Knlgmngr.DSS,
* McsEngl.klmrDss!=decision-support-system--Knlgmngr,

Knlgmngr.expert-system

description::
"overview of expert-systems:
Expert systems are a type of artificial intelligence (AI) that are designed to mimic the decision-making abilities of a human expert in a specific domain. These systems use a knowledge base of human expertise and an inference engine to draw conclusions, make recommendations, or solve problems within that domain. Here's an overview of expert systems:

1. Components of Expert Systems:
Expert systems typically consist of three main components:
a. Knowledge Base: This is where the system stores the domain-specific information and expertise. It includes facts, rules, heuristics, and other knowledge about the problem domain.
b. Inference Engine: The inference engine is responsible for applying reasoning and logic to the information stored in the knowledge base. It uses various inference methods to draw conclusions or make decisions.
c. User Interface: This component provides a means for users to interact with the expert system, ask questions, input data, and receive recommendations or solutions.

2. Knowledge Representation:
Expert systems use different knowledge representation methods, such as production rules, frames, semantic networks, and ontologies, to capture and structure domain-specific knowledge in a format that the system can understand and use.

3. Inference Methods:
Expert systems employ various inference methods to process the knowledge stored in the knowledge base. Some common techniques include forward chaining (data-driven reasoning) and backward chaining (goal-driven reasoning). These methods are used to derive conclusions or make decisions based on the available information.

4. Domain Expertise:
Expert systems are designed for specific domains or problem areas, such as medical diagnosis, financial planning, troubleshooting, and more. The knowledge in the system is tailored to that particular domain, and the system aims to replicate the decision-making abilities of a human expert within that field.

5. Advantages of Expert Systems:
- Consistency: Expert systems provide consistent and unbiased recommendations or decisions based on the encoded knowledge.
- Availability: They can be available 24/7, providing expertise whenever needed.
- Scalability: Knowledge can be easily updated and expanded as new information becomes available.
- Training: Expert systems can be used as training tools for non-experts to gain expertise in a specific domain.

6. Limitations of Expert Systems:
- Limited Domain: Expert systems are only as good as the knowledge base they are built upon and are limited to the specific domain they are designed for.
- Lack of Common Sense: They often lack common-sense reasoning abilities that humans possess.
- Maintenance: Regular maintenance and knowledge updates are necessary to keep the system relevant and accurate.
- Difficulty in Handling Uncertainty: Expert systems may struggle with handling uncertainty and making probabilistic decisions.

7. Applications:
Expert systems have been applied in various fields, including healthcare for medical diagnosis, finance for investment advice, manufacturing for quality control, customer support for troubleshooting, and more.

8. Examples:
- Dendral: One of the earliest expert systems, designed for organic chemistry.
- MYCIN: An expert system for diagnosing bacterial infections.
- TurboTax: Uses expert system principles for tax preparation.
- Watson: IBM's Watson is a famous example that combines expert systems with natural language processing to answer complex questions.

Expert systems have played a significant role in the development of AI and have found applications in a wide range of industries, helping to automate decision-making and problem-solving processes in specialized domains."
[{2023-10-28 retrieved} https://chat.openai.com/c/f9230f52-3c4a-4280-9c74-98f9377951e0]

name::
* McsEngl.ES!=expert-system--Knlgmngr,
* McsEngl.Eskm!=expert-system--Knlgmngr,
* McsEngl.Knlgmngr.019-expert-system!⇒klmrEs,
* McsEngl.Knlgmngr.expert-system!⇒klmrEs,
* McsEngl.klmrEs!=expert-system--Knlgmngr,

Knlgmngr.KMS-Knowledge-Management-System

description::
"definition of knowledge-management-system:
A knowledge management system (KMS) is a software application that helps organizations to collect, organize, store, and share knowledge. KMSs can be used to manage both explicit knowledge (such as documentation, procedures, and reports) and tacit knowledge (such as expertise, experience, and insights).

KMSs can be used to achieve a variety of business goals, such as:
* Improving customer service by providing customers with easy access to knowledge about products and services
* Reducing costs by reducing the need to reinvent the wheel or repeat mistakes
* Improving employee productivity by providing employees with easy access to knowledge they need to do their jobs effectively
* Increasing innovation by fostering collaboration and knowledge sharing
* Improving decision-making by providing decision-makers with access to relevant knowledge

KMSs can be implemented in a variety of ways, but they typically include the following features:
* A knowledge base for storing and organizing knowledge
* A search engine for finding knowledge in the knowledge base
* Collaboration features for sharing knowledge and working together on knowledge-related tasks
* Security and compliance features for protecting knowledge and ensuring compliance with relevant regulations

KMSs are used by a wide range of organizations, including businesses, educational institutions, government agencies, and nonprofit organizations.

Here are some examples of KMSs:
* Google Workspace
* Microsoft SharePoint
* Atlassian Jira Service Management
* Confluence
* Freshdesk
* ServiceNow

KMSs are a valuable tool for any organization that wants to improve its knowledge management practices and share knowledge with its employees, customers, and other stakeholders."
[{2023-10-30 retrieved} https://bard.google.com/chat/4122071e73b35c2]

name::
* McsEngl.KMS!=knowledge-managment-system--Knlgmngr,
* McsEngl.Knlgmngr.022-KMS,
* McsEngl.Knlgmngr.KMS,
* McsEngl.klmrKms!=KMS--knowledge-manager,

Knlgmngr.RBS-Rule-Based-System

description::
"overview of rule-based-systems:
Rule-based systems are a type of artificial intelligence (AI) or knowledge-based system that use a set of explicitly defined rules to make decisions or solve problems. These systems are based on a formalized set of "if-then" rules, which are designed to mimic human expertise or domain-specific knowledge. Here is an overview of rule-based systems:

1. Components of Rule-Based Systems:
- Knowledge Base: This is where the rules and domain-specific knowledge are stored. The knowledge base contains a collection of facts, assertions, and rules that govern the behavior of the system.
- Inference Engine: The inference engine is responsible for reasoning and making decisions based on the rules and facts in the knowledge base. It uses various reasoning methods, such as forward chaining (data-driven) or backward chaining (goal-driven), to draw conclusions and make inferences.
- Working Memory: Also known as the working memory or working storage, this component holds temporary data, including the current state of the system, facts, and intermediate results during the reasoning process.

2. Rule Representation:
- Rules in a rule-based system are typically represented in the form of conditional statements. Each rule consists of an "if" part (antecedent) and a "then" part (consequent). The "if" part specifies conditions that must be satisfied for the rule to be applied, while the "then" part describes the actions or conclusions to be taken if the conditions are met.

3. Inference Process:
- When a rule-based system is presented with a problem or query, the inference engine evaluates the rules and facts in the knowledge base to make decisions or provide answers.
- The inference engine may use different modes of reasoning, such as forward chaining (starting with known facts and deriving conclusions) or backward chaining (starting with a goal and working backward to determine the necessary conditions).

4. Knowledge Acquisition and Maintenance:
- Rule-based systems require human experts to encode their knowledge into the system. Knowledge acquisition can be a complex and time-consuming process.
- Maintenance of the knowledge base is also important, as the rules and facts may need to be updated over time to reflect changes in the domain or to improve the system's performance.

5. Applications of Rule-Based Systems:
- Expert Systems: Rule-based systems are often used to build expert systems that mimic human experts in specific domains, such as medical diagnosis, financial analysis, and troubleshooting.
- Decision Support Systems: Rule-based systems can be employed in decision support applications, helping users make informed decisions based on a set of rules and facts.
- Business Rule Engines: In business and industry, rule-based systems are used for automating business processes, compliance checks, and data validation.
- Natural Language Processing: Rule-based systems can also be utilized in natural language processing for tasks like grammar checking and information extraction.

6. Advantages and Limitations:
- Advantages: Rule-based systems are transparent, easy to understand, and can represent domain-specific knowledge effectively. They are well-suited for expert systems and decision support applications.
- Limitations: These systems may struggle with complex or uncertain domains, require extensive manual knowledge encoding, and may not adapt well to dynamic environments without regular updates.

In summary, rule-based systems are a class of AI systems that rely on explicitly defined rules and domain-specific knowledge to make decisions and solve problems. They have been used in a variety of applications where expert knowledge needs to be automated and provide transparency in decision-making processes."
[{2023-11-02 retrieved} https://chat.openai.com/c/30b333f6-dce7-4edc-95ce-6e7160c6d598]

name::
* McsEngl.RBS!=Rule-Based-System,
* McsEngl.Knlgmngr.028-RBS-Rule-Based-System,
* McsEngl.Knlgmngr.RBS-Rule-Based-System,
* McsEngl.klmrRule!=Rule-Based-System,

Knlgmngr.McsHitp-{2017-2023..} (link)

Knlgmngr.ReVerb-{2016-2020..}

description::
"ReVerb is a program that automatically identifies and extracts binary relationships from English sentences. ReVerb is designed for Web-scale information extraction, where the target relations cannot be specified in advance and speed is important."
[{2023-10-28 retrieved} http://reverb.cs.washington.edu/]

name::
* McsEngl.ReVerb-Knlgmngr,
* McsEngl.Knlgmngr.011-ReVerb,
* McsEngl.Knlgmngr.ReVerb,
* McsEngl.klmrReverb!=ReVerb--knowledge-manager,

addressWpg::
* https://github.com/knowitall/reverb,

Knlgmngr.ROSS-{2015..2020}

description::
"overview of ROSS digital legal expert:
ROSS Intelligence, Inc. (ROSS) is a legal technology company that has developed a digital legal expert platform. ROSS is designed to help lawyers and legal professionals find and analyze relevant legal information more quickly and efficiently.

ROSS uses artificial intelligence (AI) to understand and analyze legal documents, including statutes, case law, and regulations. ROSS can also identify relevant legal information from a variety of other sources, such as news articles, blog posts, and social media posts.

Once ROSS has identified relevant legal information, it can provide users with a variety of tools to help them analyze the information, including:
* **Legal summarization:** ROSS can provide summaries of complex legal cases and statutes, making it easier for users to understand the key points of the law.
* **Legal reasoning:** ROSS can reason about legal information and identify potential legal issues.
* **Legal research assistance:** ROSS can help users find relevant legal information for their specific cases or research projects.

ROSS is still under development, but it has the potential to revolutionize the way that lawyers and legal professionals work. ROSS can help lawyers to save time and money, and it can also help them to provide better legal services to their clients.

Here are some of the benefits of using ROSS:
* **Increased productivity:** ROSS can help lawyers to save time and money by automating the research and analysis of legal information.
* **Improved accuracy:** ROSS can help lawyers to avoid errors by providing them with accurate and up-to-date legal information.
* **Enhanced decision-making:** ROSS can help lawyers to make better decisions by providing them with insights into the legal issues that are relevant to their cases.
* **Improved client service:** ROSS can help lawyers to provide better client service by allowing them to focus on more strategic tasks, such as developing case strategies and negotiating with opposing counsel.

ROSS is a powerful tool that has the potential to revolutionize the way that lawyers and legal professionals work. It is still under development, but it is already being used by lawyers and legal professionals in a variety of industries, including corporate law, litigation, and intellectual property law."
[{2023-10-28 retrieved} https://bard.google.com/chat/eae955702a49c781]

name::
* McsEngl.ROSS-Knlgmngr,
* McsEngl.Knlgmngr.012-ROSS,
* McsEngl.Knlgmngr.ROSS,
* McsEngl.klmrRoss!=ROSS--knowledge-manager,

Knlgmngr.IBM-Watson-{2011-2015..}

description::
"overview of IBM Watson:
IBM Watson is a powerful artificial intelligence (AI) system developed by IBM. It is designed to analyze and interpret large volumes of data, including unstructured data, to provide insights, make predictions, and assist with decision-making. Here is an overview of IBM Watson:

1. **Cognitive Computing**: IBM Watson is often referred to as a cognitive computing system. Unlike traditional computers that follow explicit instructions, Watson can understand, reason, and learn from data. It can process both structured and unstructured data, such as text, images, and videos.

2. **Natural Language Processing (NLP)**: Watson is proficient in natural language processing, enabling it to understand and generate human language. It can interact with users in a conversational manner, making it suitable for applications like chatbots and virtual assistants.

3. **Machine Learning**: Watson incorporates machine learning techniques to analyze and find patterns in data. It can adapt and improve its performance over time by learning from new information and user interactions.

4. **Deep Learning**: Watson uses deep learning neural networks to process complex data, such as images and videos. This allows it to recognize objects, faces, and even emotions in multimedia content.

5. **Analytics**: Watson offers advanced data analytics capabilities. It can help organizations discover trends, correlations, and insights within their data, aiding in decision-making and problem-solving.

6. **Industries and Applications**: IBM Watson has been applied to various industries, including healthcare, finance, retail, and more. It can assist in medical diagnosis, financial risk assessment, personalized marketing, and numerous other use cases.

7. **Watson Services**: IBM provides a suite of cloud-based services under the Watson brand, such as Watson Assistant (for building chatbots and virtual agents), Watson Discovery (for content analysis and retrieval), and Watson Studio (for data science and machine learning).

8. **AI for Business**: Watson is often used by businesses to gain a competitive edge by harnessing AI and data-driven insights. It helps in automating tasks, enhancing customer experiences, and optimizing operations.

9. **Watson APIs**: Developers can access Watson's capabilities through APIs (Application Programming Interfaces) to integrate its functionalities into their own applications and services. These APIs cover various AI domains, including language understanding, speech recognition, and computer vision.

10. **Jeopardy! Challenge**: Watson gained significant public attention when it competed and won the Jeopardy! quiz show in 2011. This event showcased Watson's ability to understand and respond to natural language questions and marked a milestone in AI development.

11. **Ethical Considerations**: The use of AI, including Watson, raises ethical and privacy concerns. IBM has been actively working on ensuring that AI systems are developed and used responsibly.

12. **Ongoing Development**: IBM continues to invest in and develop the Watson platform, expanding its capabilities and offerings to meet the evolving needs of businesses and industries.

IBM Watson is a prominent player in the field of artificial intelligence, and it has been instrumental in advancing the use of AI in various sectors, providing tools and services that help organizations make better use of their data and improve decision-making processes."
[{2023-10-27 retrieved} https://chat.openai.com/c/5dbc478f-f9fc-4a94-be58-a5d42578d764]

name::
* McsEngl.IBM-Watson--Knlgmngr,
* McsEngl.Knlgmngr.009-IBM-Watson,
* McsEngl.Knlgmngr.IBM-Watson,
* McsEngl.klmrIbmwatson!=IBM-Watson--knowledge-manager,

Knlgmngr.ErgoAI-{2011..}

description::
"evolution of ErgoAI:
ErgoAI is an enterprise-level knowledge representation and reasoning system developed by Coherent Knowledge Systems. It is a dialect of F-logic with numerous extensions, including meta-programming in the style of HiLog, logical updates in the style of Transaction Logic, and defeasible reasoning.

ErgoAI evolved from the open-source Flora-2 system, which was developed by several members of the Coherent team. ErgoAI adds a number of enterprise features to Flora-2, including:
* Support for Rulelog, a more expressive logical language for semantic rules.
* An integrated development environment (IDE) for developing applications with ErgoAI.
* Support for virtualization and enterprise deployment.
* A Java interface for enterprise-grade integration.
* Python support.

ErgoAI is used in a variety of applications, including:
* Intelligent agents
* Semantic Web
* Knowledge-based networking
* Ontology management
* Integration of information
* Security policy analysis

**Evolution of ErgoAI**
ErgoAI was first released in 2011. Since then, it has undergone a number of updates and improvements. Some of the key milestones in the evolution of ErgoAI include:
* **2011:** ErgoAI 1.0 is released.
* **2012:** ErgoAI 2.0 is released, with support for Rulelog and the ErgoAI Studio IDE.
* **2014:** ErgoAI 3.0 is released, with support for virtualization and enterprise deployment.
* **2016:** ErgoAI 4.0 is released, with a Java interface and Python support.
* **2018:** ErgoAI 5.0 is released, with improved performance, scalability, and usability.
* **2020:** ErgoAI 6.0 is released, with new features for knowledge modeling, reasoning, and debugging.
* **2022:** ErgoAI 7.0 is released, with new features for knowledge sharing and collaboration.

**Future of ErgoAI**
The Coherent Knowledge team is actively developing ErgoAI. Some of the areas where they are focusing their efforts include:
* Improving the performance and scalability of ErgoAI.
* Adding new features for knowledge modeling, reasoning, and debugging.
* Making ErgoAI more accessible to a wider range of users.

Overall, ErgoAI is a powerful and versatile knowledge representation and reasoning system. It is used in a variety of applications, and it is actively being developed and improved.
Sources
flora.sourceforge.net/florahome.html
github.com/ErgoAI
[{2023-11-04 retrieved} https://bard.google.com/chat/f43236bad62515e8?hl=en&pli=1]

name::
* McsEngl.ErgoAI-Knlgmngr!⇒klmrErgoai,
* McsEngl.Knlgmngr.031-ErgoAI!⇒klmrErgoai,
* McsEngl.Knlgmngr.ErgoAI!⇒klmrErgoai,
* McsEngl.klmrErgoai!=ErgoAI--knowledge-language-manager,

Knlgmngr.AAj-{2010} (link)

Knlgmngr.ConceptNet-{2001-2019..}

description::
"overview of ConceptNet:
ConceptNet is a large-scale knowledge graph and natural language processing resource that aims to represent general human knowledge in a machine-readable format. It provides a wide range of structured data and semantic relationships that can be used to enhance various AI applications, including natural language understanding, text analysis, and reasoning. Here's an overview of ConceptNet:

1. Knowledge Graph Structure: ConceptNet is structured as a graph, where nodes represent concepts, and edges represent relationships between these concepts. Concepts can be words, phrases, or entities, and relationships can include various semantic connections.

2. Multilingual: ConceptNet is a multilingual resource, meaning it contains data in multiple languages. This makes it useful for natural language processing tasks in a global context.

3. Semantic Relations: The knowledge in ConceptNet is organized based on a wide range of semantic relationships, including "IsA" (is-a relationship), "PartOf" (part-of relationship), "HasA" (has-a relationship), "UsedFor" (used-for relationship), and many others. These relationships help capture the semantics and associations between different concepts.

4. Crowdsourced Data: ConceptNet relies on crowdsourcing for data collection. It gathers information from various sources, including contributors who annotate and validate relationships between concepts.

5. Open Source: ConceptNet is an open-source project, and its data is freely available for use in research and applications. This open nature has led to its widespread adoption and contribution from the research and development community.

6. Extensibility: Users can extend ConceptNet by adding new concepts, relationships, or languages. This allows for continuous improvement and adaptation to specific use cases.

7. Applications: ConceptNet is used in a variety of applications, including natural language understanding, sentiment analysis, chatbots, question-answering systems, and knowledge-based AI. It can help machines understand the meaning of words in context and make inferences based on the relationships between concepts.

8. Challenges: While ConceptNet is a valuable resource, it may contain errors or biases due to its crowdsourced nature. Researchers and developers using ConceptNet need to be aware of these limitations and carefully validate the data for their specific applications.

Overall, ConceptNet plays a crucial role in advancing natural language understanding and knowledge representation in AI systems. It enables machines to tap into a vast reservoir of human knowledge and make sense of the world through language."
[{2023-10-27 retrieved} https://chat.openai.com/c/19272887-1516-4edf-9978-775d8c784ba5]

name::
* McsEngl.ConceptNet-Knlgmngr,
* McsEngl.Knlgmngr.008-ConceptNet,
* McsEngl.Knlgmngr.ConceptNet,
* McsEngl.klmrConceptnet!=ConceptNet--knowledge-manager,

Knlgmngr.FrameNet-{2000-2023..}

description::
"overview of FrameNet frame-based-system:
FrameNet is a lexical resource and knowledge representation system that's designed to capture the meaning of words and phrases in the context of natural language. It is based on the idea of frames and frame semantics, a linguistic theory developed by Charles J. Fillmore. FrameNet provides a detailed and structured way to represent the meaning of words by defining them in terms of frames, frame elements, and lexical units.

Here's an overview of FrameNet and its frame-based system:

1. Frames: Frames in FrameNet are abstract representations of typical events, situations, or scenarios. Each frame is a structured conceptual framework that describes a specific type of situation and includes information about the roles and elements involved in that situation. For example, there is a "Buying" frame that describes the elements and roles in the process of purchasing something.

2. Frame Elements: Within each frame, there are frame elements, which are semantic roles that represent different participants or aspects of the frame. Frame elements are associated with specific words and phrases in the language. For instance, in the "Buying" frame, frame elements might include "Buyer," "Seller," "Goods," and "Price."

3. Lexical Units: Lexical units (LUs) are specific words or phrases in a language that are linked to frame elements within a frame. Each LU is associated with a frame and one or more frame elements. LUs capture the different ways in which a word or phrase can be used in a specific frame. For example, the word "purchase" could be a lexical unit linked to the "Buying" frame with the frame element "Goods."

4. Annotations: FrameNet includes extensive linguistic annotations of example sentences from a variety of texts. These annotations provide detailed information about how words and phrases are used in context, including their frame and frame element associations. This corpus of annotated sentences serves as the foundation for understanding word meanings and usage.

5. Applications: FrameNet has various applications in natural language processing and computational linguistics. It can be used for tasks like semantic role labeling, word sense disambiguation, and information extraction. FrameNet can also be valuable in natural language understanding, as it helps capture the meaning of words and how they are used in different contexts.

6. FrameNet Databases: FrameNet has been developed for several languages, with the most well-known being FrameNet for English. Other languages, such as Spanish and Russian, have also seen the development of FrameNet databases, each tailored to the specific characteristics of the language.

Overall, FrameNet is a valuable resource for understanding and representing the meaning of words and phrases in natural language. It provides a rich and structured framework for capturing the nuances of word usage in different contexts, making it a valuable tool for various natural language processing and linguistic applications."
[{2023-11-02 retrieved} https://chat.openai.com/c/014196e0-3d45-4cce-8e64-cfd39d0d2b56]

name::
* McsEngl.FrameNet-Knlgmngr,
* McsEngl.Knlgmngr.026-FrameNet,
* McsEngl.Knlgmngr.FrameNet,
* McsEngl.klmrFramenet!=FrameNet--knowledge-language-manager,

descriptionLong::
"What is FrameNet?
FrameNet is based on a theory of meaning called Frame Semantics, deriving from the work of Charles J. Fillmore and colleagues (Fillmore 1976, 1977, 1982, 1985, Fillmore and Baker 2001, 2010). The basic idea is straightforward: that the meanings of most words can best be understood on the basis of a semantic frame, a description of a type of event, relation, or entity and the participants in it. For example, the concept of cooking typically involves a person doing the cooking (Cook), the food that is to be cooked (Food), something to hold the food while cooking (Container) and a source of heat (Heating_instrument). In the FrameNet project, this is represented as a frame called Apply_heat, and the Cook, Food, Heating_instrument and Container are called frame elements (FEs) . Words that evoke this frame, such as fry, bake, boil, and broil, are called lexical units (LUs) of the Apply_heat frame. Other frames are more complex, such as Revenge, which involves more FEs (Offender, Injury, Injured_Party, Avenger, and Punishment) and others are simpler, such as Placing, with only an Agent (or Cause), a thing that is placed (called a Theme) and the location in which it is placed (Goal). The job of FrameNet is to define the frames and to annotate sentences to show how the FEs fit syntactically around the word that evokes the frame, as in the following examples of Apply_heat and Revenge:
... [Cook the boys] ... GRILL [Food their catches] [Heating_instrument on an open fire].
[Avenger I] 'll GET EVEN [Offender with you] [Injury for this]!

In the simplest case, the frame-evoking word is a verb and the FEs are its syntactic dependents, as in the example above where boys is the subject of the verb grill, their catches is the direct object, and on an open fire is a prepositional phrase modifying grill, but LUs can also be event nouns such as retaliation, also in the Revenge frame:
[ Punishment This attack was conducted] [Support in] RETALIATION [ Injury for the U.S. bombing raid on Tripoli... ]
or adjectives such as asleep in the Sleep frame:
[Sleeper They] [Copula were] ASLEEP [Duration for hours]

The lexical entry for each LU is derived from such annotations, and specifies the ways in which FEs are realized in syntactic structures headed by the word.

Many common nouns, such as tree, hat or tower, usually serve as dependents which head FEs, rather than clearly evoking their own frames, so we have devoted less effort to annotating them, since information about them is available from other lexicons, such as WordNet (Miller et al. 1990). We do, however, recognize that such nouns also have a minimal frame structure of their own, and in fact, the FrameNet database contains slightly more nouns than verbs.

Formally, FrameNet annotations are sets of triples that represent the FE realizations for each annotated sentence, each consisting of a frame element name (for example, Food), a grammatical function (say, Object) and a phrase type (say, noun phrase (NP)). We can think of these three types of annotation on each FE as "layers", but the grammatical function and phrase-type layers are not displayed in the web-based report system, to avoid visual clutter. The downloadable XML version of the data includes these three layers (and several more not discussed here) for all of the annotated sentences, along with complete frame and FE descriptions, frame-frame relations, and lexical entries for each annotated LU. Most of the annotations are of separate sentences annotated for only one LU, but there are also a collection of texts in which all the frame-evoking words have been annotated; the overlapping frames provide a rich representation of much of the meaning of the entire text. The FrameNet team have defined more than 1,000 semantic frames and have linked them together by a system of frame relations, which relate more general frames to more specific ones and provide a basis for reasoning about events and intentional actions.

Because the frames are basically semantic, they are often similar across languages; for example, frames about buying and selling involve the FEs Buyer, Seller, Goods, and Money, regardless of the language in which they are expressed. Several projects are underway to build FrameNets parallel to the English FrameNet project for languages around the the world, including Spanish, German, Chinese, and Japanese, and frame semantic analysis and annotation has been carried out in specialized areas from legal terminology to soccer to tourism.
26612 reads"
[{2023-11-02 retrieved} https://berkeleyfn.framenetbr.ufjf.br/WhatIsFrameNet]

Knlgmngr.CLASSIC-{1990s}

description::
"overview of classic knowledge-representation-system:
The CLASSIC knowledge representation system was developed at AT&T Bell Laboratories in the late 1980s and early 1990s. It is based on a formal logic known as Description Logics, which allows for the representation of concepts and individuals in a structured and expressive way.

CLASSIC knowledge bases are composed of two main components:
* **Concepts:** Concepts represent classes of objects or events. They are defined using a set of primitive constructors, such as conjunction, disjunction, and negation.
* **Individuals:** Individuals are instances of concepts. They are represented by names, and can have attributes and relationships to other individuals.

CLASSIC also supports a number of features that make it a powerful knowledge representation system, including:
* **Subsumption:** Subsumption is a relationship between two concepts that indicates that one concept is more general than the other. For example, the concept "Bird" subsumes the concept "Robin".
* **Classification:** Classification is the process of organizing concepts into a hierarchy based on subsumption. This hierarchy can be used to reason about the properties of individuals.
* **Reasoning:** CLASSIC supports a variety of reasoning tasks, such as determining whether a given individual is an instance of a given concept, and finding all of the concepts that subsume a given concept.

CLASSIC has been used to develop a wide range of applications, including expert systems, natural language processing systems, and software engineering tools. It was one of the first knowledge representation systems to be widely used in industry, and it continues to be an influential system today.

Here are some of the key features of the CLASSIC knowledge representation system:
* It is based on a formal logic, which makes it possible to reason about the knowledge in the system.
* It is expressive, allowing for the representation of complex concepts and relationships.
* It is scalable, able to handle large knowledge bases.
* It is efficient, able to perform reasoning tasks quickly.

CLASSIC is a powerful and versatile knowledge representation system that has been used to develop a wide range of applications. It is a good choice for applications where it is important to be able to represent and reason about complex knowledge.
[{2023-10-27 retrieved} https://bard.google.com/chat/22e9777599f0c184]

name::
* McsEngl.CLASSIC-Knlgmngr,
* McsEngl.Knlgmngr.004-CLASSIC,
* McsEngl.Knlgmngr.CLASSIC,
* McsEngl.klmrClassic!=CLASSIC--knowledge-manager,

Knlgmngr.OMCS-{1999..2016}

description::
">Open Mind Common Sense:
Open Mind Common Sense (OMCS) was an artificial intelligence project based at the Massachusetts Institute of Technology (MIT) Media Lab whose goal was to build and utilize a large commonsense knowledge base from the contributions of many thousands of people across the Web. It was active from 1999 to 2016.

OMCS was designed to collect common-sense knowledge from the general public over the web. The project's website allowed users to submit facts, descriptions, and stories about the world around them. These submissions were then reviewed by other users to ensure their accuracy and relevance.

OMCS collected over a million facts from over 15,000 contributors, making it one of the largest commonsense knowledge bases in existence. The project also developed a number of algorithms for processing and reasoning about common-sense knowledge.

OMCS was a valuable resource for artificial intelligence research, and it helped to advance the state of the art in commonsense reasoning. However, the project was ultimately discontinued due to a lack of funding.

Here are some of the key features of OMCS:
* **Crowdsourced knowledge acquisition:** OMCS collected knowledge from the general public over the web. This allowed the project to gather a large amount of data that would have been difficult or impossible to collect otherwise.
* **Natural language processing:** OMCS used natural language processing techniques to extract information from text submissions. This allowed the project to collect a wider range of information than would have been possible with manual data entry.
* **Collaborative knowledge validation:** OMCS submissions were reviewed by other users to ensure their accuracy and relevance. This helped to ensure the quality of the knowledge base.
* **Commonsense reasoning algorithms:** OMCS developed a number of algorithms for processing and reasoning about common-sense knowledge. These algorithms were used to make inferences from the knowledge base and to answer questions about the world.

OMCS was a pioneering project in the field of artificial intelligence. It demonstrated the feasibility of collecting common-sense knowledge from the general public and the value of such knowledge for AI applications. The project's legacy continues to inspire researchers today.
Sources
en.wikipedia.org/wiki/Open_Mind_Common_Sense"
[{2023-11-05 retrieved} https://bard.google.com/chat/94786d097d09bd84?hl=en&pli=1]

name::
* McsEngl.OMCS!=open-mind-common-sense--Knlgmngr,
* McsEngl.Knlgmngr.032-OMCS-{1999..2016},
* McsEngl.Knlgmngr.OMCS-{1999..2016},
* McsEngl.Knlgmngr.{1999..2016}-OMCS,

Knlgmngr.ThoughtTreasure-{1995-2006-2023..}

description::
"overview of ThoughtTreasure:
ThoughtTreasure is a commonsense knowledge base and architecture for natural language processing (NLP). It was developed by Erik T. Mueller and his team at the University of California, Berkeley.

ThoughtTreasure is a knowledge base of over 25,000 commonsense concepts, such as objects, actions, events, and relationships. Each concept is represented in a variety of ways, including logic, finite automata, grids, and scripts.

ThoughtTreasure also includes an architecture for NLP that uses the knowledge base to perform a variety of tasks, such as:
* **Text generation:** ThoughtTreasure can generate text in a variety of formats, including poems, code, scripts, musical pieces, email, letters, etc. It can also translate languages and write different kinds of creative content.
* **Question answering:** ThoughtTreasure can answer questions in a comprehensive and informative way, even if they are open ended, challenging, or strange. It can also provide summaries of factual topics or create stories.
* **Inference:** ThoughtTreasure can make inferences about the world based on its knowledge base. For example, if ThoughtTreasure knows that a bird is an animal and that animals have wings, it can infer that a bird has wings.
* **Commonsense reasoning:** ThoughtTreasure can use its knowledge base to reason about commonsense scenarios. For example, if ThoughtTreasure knows that a person is likely to be in the kitchen if they are making a sandwich, it can infer that the person is likely to be in the kitchen if they are looking for a knife.

ThoughtTreasure is a powerful tool for NLP, and it has been used to develop a variety of applications, such as:
* **Chatbots:** ThoughtTreasure can be used to develop chatbots that can converse with humans in a natural and engaging way.
* **Question answering systems:** ThoughtTreasure can be used to develop question answering systems that can provide comprehensive and informative answers to a wide range of questions.
* **Text summarization systems:** ThoughtTreasure can be used to develop text summarization systems that can provide summaries of factual topics or create stories.
* **Machine translation systems:** ThoughtTreasure can be used to develop machine translation systems that can translate languages more accurately and fluently.

ThoughtTreasure is an active research project, and it is constantly being improved. It is likely to play a major role in the future of NLP and artificial intelligence."
[{2023-10-28 retrieved} https://bard.google.com/chat/a255212365945ab1]

name::
* McsEngl.ThoughtTreasure-Knlgmngr,
* McsEngl.Knlgmngr.016-ThoughtTreasure,
* McsEngl.Knlgmngr.ThoughtTreasure,
* McsEngl.klmrThoughttreasure!=ThoughtTreasure--knowledge-manager,

addressWpg::
* https://github.com/eriktmueller/thoughttreasure,

Knlgmngr.TROEPS-{1995-1999}

description::
"TROEPS is an object based knowledge representation system. It thus allows to model particular domains under the form of objects. This means that the individual entities of the domain are seen as discrete entities with fields denoting their characteristics and their relationship with other such entities. These objects are also grouped into families of objects called concepts or classes which, in turn, can be related to each others by specialisation or attributive relationship. Each construct of the system has a precise semantics which allows to check the models for internal consistency.
TROEPS knowledge bases can be explored in order to find the objects and their relationships. To that extent, turning a knowledge base into a Web knowledge server, either for browsing, editing or exploiting the model, has been made easy. Moreover, lexicons and annotations enable to document the model straightforwardly.
However, the interpretation and exploitation of the model is left to external programs (generally through the TROEPS API). The system provides basic tools for easing the use of models such as sophisticated field value computation, instance classification and conceptual clustering.
This Reference manual provides an in depth presentation of the TROEPS system. It can be found on-line at http://co4.inrialpes.fr/docs/troeps-manual.html.
[{2023-10-28 retrieved} https://hytropes.inrialpes.fr/docs/troeps-1.3a/troeps-Forewor.html]

name::
* McsEngl.TROEPS-Knlgmngr,
* McsEngl.Knlgmngr.017-TROEPS,
* McsEngl.Knlgmngr.TROEPS,

Knlgmngr.Ontolingua-{1993-2005-2023}

description::
"overview of Ontolingua:
Ontolingua is a knowledge representation language and associated software tools used for knowledge engineering and building ontologies. It was developed at Stanford University's Knowledge Systems Laboratory in the 1990s and has been influential in the field of artificial intelligence, particularly in the area of formal ontology development. Here's an overview of Ontolingua:

1. **Knowledge Representation Language**: Ontolingua provides a language for representing knowledge and building ontologies. It is a formal language that allows users to define concepts, relationships, and constraints in a structured and machine-readable manner.

2. **Ontologies**: Ontolingua is often used to create ontologies, which are formal and explicit specifications of the concepts, relationships, and constraints within a particular domain. These ontologies help in capturing and organizing knowledge in a systematic and standardized way.

3. **Expressiveness**: Ontolingua is designed to be expressive, allowing users to define complex relationships and constraints. It supports a variety of constructs for modeling domain-specific knowledge, including classes, slots, and relations.

4. **Inheritance**: Ontolingua supports the notion of inheritance, allowing concepts to inherit properties and relationships from parent concepts. This makes it easier to create hierarchical and structured ontologies.

5. **Modularity**: Ontolingua promotes the development of modular ontologies, where different aspects of a domain's knowledge can be organized into separate modules. This enhances reusability and maintainability.

6. **Knowledge Sharing**: Ontolingua facilitates the sharing of knowledge by providing a standardized representation that can be understood by both humans and machines. This is valuable for collaborative knowledge engineering projects and interoperability between different systems.

7. **Software Tools**: In addition to the language, Ontolingua includes software tools for creating, editing, and managing ontologies. These tools help knowledge engineers and domain experts work collaboratively to build and maintain ontologies.

8. **Interoperability**: Ontolingua was designed with an emphasis on interoperability, allowing ontologies to be used in various AI and knowledge-based systems. It supports various export formats, including KIF (Knowledge Interchange Format) and OWL (Web Ontology Language).

9. **Applications**: Ontolingua has been used in various domains, including biomedical informatics, knowledge management, natural language processing, and semantic web applications. Its ability to formalize domain-specific knowledge has made it useful in a wide range of applications.

10. **Legacy**: While Ontolingua was influential in its time, the field of ontology development and knowledge representation has evolved, and other languages and tools, such as OWL and Protιgι, have gained prominence. However, Ontolingua's concepts and principles have had a lasting impact on the development of ontologies and knowledge engineering.

In summary, Ontolingua is a knowledge representation language and associated tools that have played a significant role in the development of formal ontologies and knowledge engineering. It emphasizes the structured and systematic representation of knowledge, making it valuable for various AI and knowledge-based applications."
[{2023-10-28 retrieved} https://chat.openai.com/c/5dbc478f-f9fc-4a94-be58-a5d42578d764]

name::
* McsEngl.Ontolingua-Knlgmngr,
* McsEngl.Knlgmngr.013-Ontolingua,
* McsEngl.Knlgmngr.Ontolingua,
* McsEngl.klmrOntolingua!=Ontolingua--knowledge-manager,

Knlgmngr.PowerLoom-{1993-2015}

description::
"overview of PowerLoom:
PowerLoom is a knowledge representation and reasoning system that was developed at the University of Maryland. It is designed to represent and manipulate complex knowledge, making it particularly useful for applications involving artificial intelligence, expert systems, and knowledge-based systems. PowerLoom is built on a foundation of first-order logic, which allows it to represent and reason about a wide range of concepts and relationships.

Here's an overview of some key features and aspects of PowerLoom:

1. Knowledge Representation: PowerLoom uses a knowledge representation language that is based on first-order predicate logic. This allows it to express complex relationships, rules, and facts in a structured and formal way. It supports the creation of ontologies, which are used to model the concepts and their interconnections in a domain.

2. Inference Engine: PowerLoom includes a powerful inference engine that can perform various forms of reasoning, including deductive, inductive, and abductive reasoning. It supports rule-based and frame-based knowledge representation, allowing it to capture a wide range of knowledge structures.

3. Ontologies: PowerLoom facilitates the development of ontologies, which are hierarchical structures that organize knowledge in a domain. Ontologies help in defining classes, subclasses, and relationships among different entities. PowerLoom can also perform reasoning over these ontologies to derive new information.

4. Rules and Constraints: Users can define rules and constraints in PowerLoom to capture domain-specific knowledge. These rules can be used to make inferences and draw conclusions based on the existing knowledge base.

5. Knowledge Bases: PowerLoom allows you to build and manage knowledge bases that contain a collection of facts, rules, and concepts. These knowledge bases can be updated and queried to retrieve information.

6. Natural Language Interface: PowerLoom provides a natural language interface that allows users to interact with the system using human-readable language, making it more accessible to a wider range of users.

7. Scalability: PowerLoom is designed to handle large and complex knowledge bases efficiently. Its architecture allows for scaling to handle significant amounts of data.

8. Applications: PowerLoom has been used in a variety of applications, including expert systems, intelligent tutoring systems, semantic web technologies, and medical knowledge representation. It is particularly suitable for tasks that require sophisticated reasoning and decision-making capabilities.

9. Open Source: PowerLoom is available as open-source software, making it accessible for researchers and developers to use, extend, and customize for their specific needs.

Overall, PowerLoom is a versatile knowledge representation and reasoning system that is designed to help computers understand and work with complex, structured knowledge. Its ability to handle ontologies, rules, and inference makes it a valuable tool for building intelligent systems and expert applications in various domains."
[{2023-10-28 retrieved} https://chat.openai.com/c/1d20565d-e583-4baa-9566-3b92452e06dd]
===
"overview of PowerLoom:
**PowerLoom** is a knowledge representation and reasoning system that was developed at the University of Southern California Information Sciences Institute. It is a successor to the Loom system, and it provides a number of features that make it a powerful tool for knowledge management and artificial intelligence applications.

PowerLoom is based on a fully expressive, logic-based representation language. It uses a natural deduction inference engine that combines forward and backward chaining to derive what logically follows from the facts and rules asserted in the knowledge base.

PowerLoom also provides a number of other features, including:
* **Modularity:** Knowledge bases in PowerLoom can be modularized, making them easier to reuse and maintain.
* **Ultra-lightweight worlds:** PowerLoom supports hypothetical reasoning using ultra-lightweight worlds.
* **Description classification:** PowerLoom includes a description classifier that can classify descriptions expressed in full first-order predicate calculus.
* **Programming language:** PowerLoom is written in a new programming language called STELLA, which is a strongly typed, Lisp-like language. This makes it possible to extend PowerLoom with new features and functionality.

PowerLoom has been used to develop a variety of knowledge-based applications, including:
* **Expert systems:** PowerLoom has been used to develop expert systems for a variety of domains, including medical diagnosis, financial planning, and legal research.
* **Decision support systems:** PowerLoom has been used to develop decision support systems for a variety of applications, such as military planning, business intelligence, and environmental management.
* **Natural language processing systems:** PowerLoom has been used to develop natural language processing systems that can understand and generate human language.
* **Semantic web applications:** PowerLoom has been used to develop semantic web applications that can reason about the meaning of data on the web.

PowerLoom is a powerful and versatile knowledge representation and reasoning system. It is still under development, but it has already been used to develop a wide range of applications in a variety of domains.

Here are some of the benefits of using PowerLoom:
* **Improved knowledge management:** PowerLoom can help to improve knowledge management by providing a way to organize and represent knowledge in a structured and logical way.
* **Enhanced decision-making:** PowerLoom can help to improve decision-making by providing a way to reason about complex knowledge bases and to identify potential problems and opportunities.
* **Increased productivity:** PowerLoom can help to increase the productivity of knowledge workers by automating tasks such as knowledge acquisition, storage, retrieval, and sharing.
* **Improved collaboration:** PowerLoom can help to improve collaboration between knowledge workers by providing a common language for defining and reasoning about knowledge.

Overall, PowerLoom is a powerful tool that can be used to improve the way that organizations manage and use their knowledge.
Sources books.google.com/books?id=WG6EEN_F3acC"
[{2023-10-28 retrieved} https://bard.google.com/chat/eae955702a49c781]

name::
* McsEngl.PowerLoom-Knlgmngr,
* McsEngl.Knlgmngr.014-PowerLoom,
* McsEngl.Knlgmngr.PowerLoom,
* McsEngl.klmrPowerloom!=PowerLoom--knowledge-manager,

Knlgmngr.Algernon-{1992-2022}

description::
"overview of Algernon knowledge-representation-system:
Algernon is a knowledge-representation system that was developed in the early 1990s. It is based on a theory called Access-Limited Logic (ALL), which formalizes the access limitations inherent in a network-structured knowledge base.

The Algernon system has evolved over time to incorporate new features and improve its performance. Some of the key changes that have been made include:
* **Improved reasoning capabilities:** Algernon can now handle a wider range of reasoning tasks, including non-monotonic reasoning and reasoning under uncertainty.
* **Enhanced expressiveness:** Algernon's knowledge representation language has been extended to allow for more expressive representations of knowledge.
* **Improved scalability:** Algernon can now handle larger and more complex knowledge bases.
* **Support for new applications:** Algernon has been used to develop a wide range of applications, including natural language processing systems, expert systems, and intelligent agents.

Here is a more detailed overview of the evolution of the Algernon knowledge-representation system:
* **1992:** The Algernon system is first developed by David Poole.
* **1994:** Algernon is extended to support non-monotonic reasoning.
* **1996:** Algernon is extended to support reasoning under uncertainty.
* **1998:** Algernon is extended to support more expressive representations of knowledge.
* **2000:** Algernon is extended to improve its scalability.
* **2002:** Algernon is used to develop a natural language processing system called AlgernonNL.
* **2004:** Algernon is used to develop an expert system called AlgernonX.
* **2006:** Algernon is used to develop an intelligent agent called AlgernonAgent.
* **2008:** Algernon is released as an open source project.
* **2010:** Algernon is extended to support new knowledge representation languages, such as OWL and RDF.
* **2012:** Algernon is used to develop a semantic web application called AlgernonSW.
* **2014:** Algernon is extended to support big data applications.
* **2016:** Algernon is used to develop a machine learning system called AlgernonML.
* **2018:** Algernon is extended to support distributed reasoning.
* **2020:** Algernon is used to develop a knowledge graph system called AlgernonKG.
* **2022:** Algernon is extended to support explainable AI.

Today, Algernon is a powerful and versatile knowledge-representation system that is used to develop a wide range of applications. It is a valuable tool for researchers and practitioners who are working on artificial intelligence, natural language processing, and machine learning.

## Future directions
The future of the Algernon knowledge-representation system is bright. Researchers are working on a number of new features and enhancements, including:
* **Support for new reasoning tasks:** Algernon is being extended to support new reasoning tasks, such as abductive reasoning and analogical reasoning.
* **Improved efficiency:** Algernon is being optimized to improve its efficiency on large and complex knowledge bases.
* **Enhanced usability:** Algernon is being made more user-friendly and accessible to a wider range of users.

Algernon is also being integrated with other AI technologies, such as machine learning and deep learning. This will allow Algernon to be used to develop even more sophisticated and powerful applications.
[{2023-10-27 retrieved} https://bard.google.com/chat/c280dc55b3d3942c]

name::
* McsEngl.Algernon-Knlgmngr,
* McsEngl.klmrAlgernon!=Algernon--knowledge-manager,
* McsEngl.Knlgmngr.001-Algernon,
* McsEngl.Knlgmngr.Algernon,

Knlgmngr.GALEN-{1991-2020}

description::
"overview of GALEN system of University of Manchester:
GALen (Generalized Architecture for Languages, Encyclopedias, and Nomenclatures in Medicine) is a comprehensive medical terminology and knowledge management system developed at the University of Manchester. It is designed to support various aspects of healthcare, including clinical practice, research, and healthcare information systems. GALen was developed with the goal of providing a standardized and structured approach to medical terminology and knowledge representation.

Here's an overview of the GALEN system:
1. Terminology Standardization: GALEN focuses on standardizing and formalizing medical terminology. It provides a framework for representing medical concepts, including diseases, symptoms, drugs, procedures, and more, in a consistent and computable manner.

2. Knowledge Representation: GALEN uses a knowledge representation system to model medical concepts and relationships between them. This allows for more precise and semantically rich descriptions of medical data.

3. Ontology Development: GALEN includes a comprehensive ontology that defines the structure and relationships of medical concepts. This ontology is designed to be used in healthcare information systems to enhance interoperability and data integration.

4. Clinical Decision Support: GALEN supports the development of clinical decision support systems by providing a structured knowledge base for healthcare professionals. It can be used to build tools that aid in diagnosis, treatment recommendations, and other clinical decision-making processes.

5. Terminology Mapping: GALEN provides tools and resources for mapping between different medical terminologies and classifications. This is essential for data exchange and collaboration between healthcare organizations and systems that use different terminologies.

6. Research and Development: The GALEN project has contributed to various research areas in medical informatics and healthcare, including the development of terminologies, ontologies, and decision support systems. It has also played a role in standardization efforts within the healthcare industry.

7. Clinical Data Integration: GALEN's structured terminology and knowledge representation enable the integration of diverse clinical data sources, making it easier to analyze and share healthcare information.

It's important to note that GALEN has been involved in various research and development projects over the years, and its influence extends beyond the University of Manchester. The system has contributed to the field of medical informatics and the development of international standards for healthcare terminologies and ontologies.

Please note that the specific capabilities and applications of GALEN may have evolved since my last knowledge update in January 2022, and you may want to consult more recent sources for the latest information on the system."
[{2023-10-27 retrieved} https://chat.openai.com/c/96008a08-e9ce-4554-8acb-e8502cda2f62]

name::
* McsEngl.GALEN-Knlgmngr,
* McsEngl.Knlgmngr.007-GALEN,
* McsEngl.Knlgmngr.GALEN,
* McsEngl.klmrGalen!=GALEN--knowledge-manager,

Knlgmngr.ConceptBase-{1987-2013..}

description::
"overview of ConceptBase:
ConceptBase is a multi-user deductive object manager intended for conceptual modeling, metamodeling, and coordination in design environments. It is a database system for storing interdependent models at any abstraction level. ConceptBase utilizes the object-oriented principles of instantiation, specialization, and attribution/association to represent models. Its logic-based rule and constraint language allows circumscribing the intended semantics of classes, meta classes, meta meta classes, and so forth.

ConceptBase was originally conceived in the 1980s as a repository system maintaining heterogeneous models created during the development of data-intensive applications, i.e. software systems that rely on database systems as one of their components.

**Features:**
* **Multi-user:** ConceptBase supports multiple users to concurrently access and modify the same model.
* **Deductive:** ConceptBase can infer new knowledge from existing knowledge using deductive rules.
* **Object-oriented:** ConceptBase uses object-oriented principles to represent models, such as instantiation, specialization, and attribution/association.
* **Logic-based:** ConceptBase has a logic-based rule and constraint language that allows users to define the semantics of their models.
* **Metamodeling:** ConceptBase can be used to model other models, which is known as metamodeling.

**Benefits:**
* **Improved modeling quality:** ConceptBase's deductive and logic-based features can help users to improve the quality of their models by identifying and eliminating inconsistencies.
* **Reduced development time:** ConceptBase's metamodeling capabilities can help users to reduce development time by allowing them to reuse existing models and modeling patterns.
* **Improved communication and collaboration:** ConceptBase's multi-user support can help users to communicate and collaborate more effectively on their models.

**Applications:**
* **Conceptual modeling:** ConceptBase can be used to develop conceptual models of information systems, software systems, and other complex systems.
* **Metamodeling:** ConceptBase can be used to model other models, such as domain models, process models, and architecture models.
* **Method engineering:** ConceptBase can be used to develop and manage modeling methods.
* **Knowledge representation:** ConceptBase can be used to represent and manage knowledge in a variety of domains, such as medicine, finance, and engineering.

Overall, ConceptBase is a powerful tool for modeling, metamodeling, and knowledge representation. It is used by a variety of organizations in academia and industry, including Google, Microsoft, and IBM.
Sources
www.researchgate.net/figure/Buttons-of-the-History-Window_fig5_326352578
modeling-languages.com/conceptbase-database-system-metamodeling/"
[{2023-10-27 retrieved} https://bard.google.com/chat/db619d6544f8c301]

name::
* McsEngl.ConceptBase-Knlgmngr,
* McsEngl.Knlgmngr.006-ConceptBase,
* McsEngl.Knlgmngr.ConceptBase,
* McsEngl.klmrConceptbase!=ConceptBase-knowledge-manager,

Knlgmngr.WordNet-{1985-2011}

description::
"overview of WordNet:
WordNet is a lexical database and a comprehensive lexical reference system for the English language. It was developed at Princeton University and is widely used in natural language processing, artificial intelligence, and computational linguistics. WordNet provides a structured and detailed inventory of words and their semantic relationships. Here's an overview of WordNet:

1. **Lexical Database:** WordNet is a lexical database, which means it's a repository of words and their various aspects, such as definitions, synonyms, antonyms, part-of-speech information, and more. It doesn't just store words in isolation but also provides information about how they relate to each other.

2. **Hierarchy of Concepts:** One of the key features of WordNet is the hierarchical organization of words and concepts. It groups related words into synsets (synonym sets) and organizes these synsets into a hypernym-hyponym hierarchy. This means that words are organized into broader and narrower categories, helping to understand the relationships between words and concepts.

3. **Synonyms and Antonyms:** WordNet provides synonyms and antonyms for many words, making it a valuable resource for natural language understanding and generation tasks.

4. **Semantic Relations:** It defines various semantic relations between words and synsets. Some of the common semantic relations in WordNet include meronymy (part-whole relationships), holonymy (whole-part relationships), hypernymy (is-a relationships), and more. These relationships help in understanding the semantic structure of the English language.

5. **Part of Speech:** WordNet includes information about the part of speech of each word, such as whether a word is a noun, verb, adjective, or adverb. This information is essential for many natural language processing tasks.

6. **Applications:** WordNet has been used in various applications, including machine translation, information retrieval, information extraction, and sentiment analysis. It's also used as a resource for building semantic networks and knowledge graphs.

7. **Word Sense Disambiguation:** WordNet is helpful for word sense disambiguation, which is the process of determining the correct meaning of a word in a given context. By providing multiple senses for a word and their relationships, WordNet aids in disambiguating word meanings.

WordNet is a valuable resource for researchers and developers working on natural language understanding and generation. It has not only been influential in the development of NLP algorithms but has also contributed to our understanding of lexical semantics and language structure. Note that while WordNet primarily focuses on English, similar projects exist for other languages."
[{2023-10-28 retrieved} https://chat.openai.com/c/8600496d-550c-4407-bb78-e1c4a45755e5]

name::
* McsEngl.Wnkm!=WordNet-Knlgmngr,
* McsEngl.WordNet-Knlgmngr!⇒Wnkm,
* McsEngl.Knlgmngr.018-WordNet!⇒Wnkm,
* McsEngl.Knlgmngr.WordNet!⇒Wnkm,
* McsEngl.wordnet!⇒Wnkm,

descriptionLong::
"a semantic lexicon for the English language"
[{2023-09-18 retrieved} https://en.wikipedia.org/wiki/Hyponymy_and_hypernymy]
===
"WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. The resulting network of meaningfully related words and concepts can be navigated with the browser(link is external). WordNet is also freely and publicly available for download. WordNet's structure makes it a useful tool for computational linguistics and natural language processing.
WordNet superficially resembles a thesaurus, in that it groups words together based on their meanings. However, there are some important distinctions. First, WordNet interlinks not just word forms—strings of letters—but specific senses of words. As a result, words that are found in close proximity to one another in the network are semantically disambiguated. Second, WordNet labels the semantic relations among words, whereas the groupings of words in a thesaurus does not follow any explicit pattern other than meaning similarity."
[{2021-02-02} https://wordnet.princeton.edu/]
===
"Some early knowledge graphs were topic-specific. In 1985, Wordnet was founded, capturing semantic relationships between words and meanings – an application of this idea to language itself."
[{2021-01-31} https://en.wikipedia.org/wiki/Knowledge_graph]

Knlgmngr.CYC-{1984-1995-2023} (link)

Knlgmngr.KL-ONE-{1982}

description::
× generic: frame-language--manager,

"overview of KL-ONE:
KL-ONE is a knowledge representation system in the tradition of semantic networks and frames. It is a frame language that provides a structured inheritance network for representing conceptual information.
*
* **Concepts** in KL-ONE are the basic building blocks of knowledge representation. Concepts can be primitive, meaning that they are not further defined, or defined, meaning that they are defined in terms of other concepts.
*
* **Roles** are relationships between concepts. For example, the concept "Person" might have a role called "has-parent", which would relate a person to their parent.
*
* **Descriptions** in KL-ONE are used to represent the properties and relationships of concepts. A description can be simple, such as "Person has-parent Person", or it can be complex, such as "Person who is a parent of two or more children."
*
* **Inheritance** in KL-ONE is used to organize concepts into a hierarchy. For example, the concept "Person" might inherit from the concept "Animal". This means that everything that is true of an animal is also true of a person.
*
* **KL-ONE also supports multiple inheritance**, which means that a concept can inherit from multiple other concepts. This allows for more complex and nuanced representations of knowledge.
*
* **KL-ONE has a number of features that make it a powerful knowledge representation system:**
* * **Structured inheritance network:** KL-ONE's structured inheritance network allows for efficient reasoning about knowledge.
* * **Complex descriptions:** KL-ONE's complex descriptions allow for the representation of a wide range of knowledge, from simple facts to complex relationships.
* * **Multiple inheritance:** KL-ONE's multiple inheritance allows for more flexible and expressive knowledge representation.
* * **Deductive classifier:** KL-ONE includes a deductive classifier that can be used to validate knowledge bases and deduce new knowledge.
*
* KL-ONE has been used in a variety of applications, including natural language processing, knowledge engineering, and machine learning.
*
* Here is an example of a KL-ONE description:
* ```
* (Concept Person)
* (Subsumes Person Animal)
* (Role has-parent)
* (Description Person who has-parent Person)
* ```
*
* This description says that the concept "Person" subsumes the concept "Animal" and that persons have parents. It also says that a person is defined as someone who has a parent who is also a person.
*
* KL-ONE is a powerful knowledge representation system that can be used to represent a wide range of knowledge. It is a good choice for applications where complex and flexible knowledge representation is needed."
[{2023-10-30 retrieved} https://bard.google.com/chat/1ad6ed38e0932b22]

name::
* McsEngl.KL-ONE-Knlgmngr!⇒klmrKlone,
* McsEngl.klmrKlone!=KL-ONE--knowledge-manager,
* McsEngl.Knlgmngr.021-KL-ONE!⇒klmrKlone,
* McsEngl.Knlgmngr.KL-ONE!⇒klmrKlone,

Knlgmngr.MYCIN-{1974..1990s}

description::
"overview of MYCIN:
MYCIN was an early expert system developed in the 1970s at Stanford University. It was designed to help physicians diagnose and treat bacterial infections. MYCIN was a backward chaining expert system, meaning that it started with a hypothesis and then asked questions to confirm or disconfirm that hypothesis.

MYCIN's knowledge base consisted of over 500 production rules. Each production rule represented a piece of knowledge about bacterial infections, such as "If the patient has a fever and a headache, then they may have meningitis." MYCIN would use its knowledge base to ask the physician a series of questions about the patient's symptoms and medical history. Based on the physician's answers, MYCIN would generate a list of possible diagnoses.

MYCIN would then recommend a course of treatment for each diagnosis. The treatment recommendations were based on MYCIN's knowledge of the bacteria that cause each infection, the antibiotics that are effective against those bacteria, and the patient's individual characteristics, such as their age and weight.

MYCIN was evaluated in a number of clinical trials, and it was found to be as accurate as human experts in diagnosing bacterial infections. MYCIN was also found to be more consistent in its treatment recommendations than human experts.

MYCIN was never widely deployed in clinical practice, but it was a landmark achievement in the field of artificial intelligence. MYCIN is considered to be the "granddaddy of all expert systems."

Here are some of the key features of MYCIN:
* **Backward chaining:** MYCIN used backward chaining to reason from a hypothesis to a conclusion.
* **Production rules:** MYCIN's knowledge base was represented as a set of production rules.
* **Expert knowledge:** MYCIN's knowledge base was developed by consulting with experts in the field of infectious disease.
* **Consistency:** MYCIN was more consistent in its treatment recommendations than human experts.

MYCIN was a pioneering expert system that helped to lay the foundation for the field of knowledge-based systems. MYCIN's legacy can be seen in the many expert systems that are used today in a variety of domains, such as medical diagnosis, financial planning, and fraud detection."
[{2023-10-27 retrieved} https://bard.google.com/chat/2afefc8b86a6d9bd]

name::
* McsEngl.Knlgmngr.002-MYCIN,
* McsEngl.Knlgmngr.MYCIN,
* McsEngl.MYCIN-Knlgmngr,
* McsEngl.klmrMycin!=MYCIN--knowledge-manager,

Knlgmngr.CODE4

description::
"CODE4 (Conceptually Oriented Description Environment) is a general purpose knowledge management system intended for analysing, debugging, and delivering knowledge about some domain. It is designed to be easily adaptable to many applications, such as natural language processing, software specification and design, expert systems, general terminological analysis, or teaching subjects such as biology or Unix. It can be learned by non-computer people in a few days."
[{2023-10-27 retrieved} https://www.site.uottawa.ca/kaml/CODE4.html]

"CODE4 knowledge-management-system:
CODE4 is a general-purpose knowledge management system (KMS) that can be used to store, retrieve, and analyze conceptual knowledge. It is intended to be easy to use and flexible enough to meet the needs of a variety of users.

CODE4 has a number of features that make it a powerful KMS:
* **Conceptual modeling:** CODE4 allows users to model their knowledge in a way that is both expressive and easy to understand. This makes it possible to store and retrieve knowledge in a way that is relevant to the user's needs.
* **Automatic inferencing:** CODE4 can automatically infer new knowledge from the knowledge that is stored in the system. This allows users to gain new insights from their knowledge without having to explicitly encode them.
* **Collaboration:** CODE4 supports collaboration between users on knowledge management tasks. This makes it possible to share knowledge and expertise more effectively.
* **Extensibility:** CODE4 is extensible, so users can add new features and functionality to the system to meet their specific needs.

CODE4 has been used in a variety of applications, including:
* **Product design:** CODE4 can be used to store and retrieve knowledge about products, such as their specifications, design constraints, and usage scenarios. This information can be used to improve the design of new products and to troubleshoot problems with existing products.
* **Software development:** CODE4 can be used to store and retrieve knowledge about software, such as its design, implementation, and testing. This information can be used to improve the development process and to ensure the quality of software products.
* **Customer service:** CODE4 can be used to store and retrieve knowledge about customers, such as their preferences, past interactions with the company, and unresolved issues. This information can be used to provide better customer service and to resolve customer problems more quickly.

Overall, CODE4 is a powerful and versatile KMS that can be used to improve knowledge management in a variety of organizations."
[{2023-10-27 retrieved} https://bard.google.com/chat/a911ade8e47b3291]

name::
* McsEngl.CODE4-Knlgmngr,
* McsEngl.Knlgmngr.005-CODE4,
* McsEngl.Knlgmngr.CODE4,

descriptionLong::
"overview of CODE4 KMS:
CODE4 KMS is a cloud-based knowledge management system developed by the Google AI team. It is a powerful tool that helps organizations to collect, organize, and share knowledge. CODE4 KMS is easy to use and navigate, and it includes a number of features to help users to find the knowledge they need quickly and easily.

Here are some of the key features of CODE4 KMS:
* **Natural language processing (NLP):** CODE4 KMS supports NLP, which allows users to search for knowledge using natural language queries. For example, a user could type in the query "What is the best way to treat a migraine?" and CODE4 KMS would return relevant knowledge articles, documentation, and other resources.
* **Machine learning (ML):** CODE4 KMS also supports ML, which allows users to train the system to identify and extract knowledge from unstructured data sources. This can be useful for extracting knowledge from emails, chat logs, and other types of unstructured data.
* **Knowledge graph:** CODE4 KMS includes a knowledge graph, which is a network of interconnected nodes that represent knowledge entities. The knowledge graph can be used to browse and explore knowledge in a more visual way.
* **Collaboration features:** CODE4 KMS includes collaboration features that allow users to share knowledge with each other, annotate knowledge articles, and create discussion forums.
* **Security and compliance:** CODE4 KMS includes security and compliance features that help organizations to protect their knowledge and ensure compliance with relevant regulations.

CODE4 KMS is a valuable tool for a wide range of organizations, including:
* **Businesses:** CODE4 KMS can be used by businesses to improve their knowledge management practices, share knowledge with employees and customers, and improve decision-making.
* **Educational institutions:** CODE4 KMS can be used by educational institutions to collect and organize knowledge for students and faculty, and to improve the teaching and learning process.
* **Government agencies:** CODE4 KMS can be used by government agencies to collect and organize knowledge about laws, regulations, and policies, and to improve the delivery of government services.
* **Nonprofit organizations:** CODE4 KMS can be used by nonprofit organizations to collect and organize knowledge about their programs and services, and to share knowledge with their stakeholders.

Overall, CODE4 KMS is a powerful and versatile knowledge management system that can be used by organizations of all sizes to improve their knowledge management practices and share knowledge with their employees, customers, and other stakeholders."
[{2023-10-30 retrieved} https://bard.google.com/chat/4122071e73b35c2]

Knlgmngr.IKARUS

description::
"overview of Intelligent knowledge acquisition and retrieval universal system:
**Intelligent Knowledge Acquisition and Retrieval Universal System (IKARUS)** is a knowledge management system that aims to provide a comprehensive and integrated solution for knowledge acquisition, representation, storage, retrieval, and sharing. It is designed to be a universal system that can be used in a wide variety of domains, including business, education, healthcare, and government.

IKARUS is based on a number of key principles, including:
* **Semantic knowledge representation:** IKARUS uses a semantic knowledge representation model that allows it to represent the meaning of knowledge in a way that is both machine-readable and human-understandable. This makes it possible for IKARUS to reason about knowledge and to generate new knowledge based on existing knowledge.
* **Intelligent knowledge acquisition:** IKARUS provides a number of intelligent knowledge acquisition tools that can be used to extract knowledge from a variety of sources, including unstructured text, databases, and expert systems. This makes it possible to create and maintain large knowledge bases without the need for manual data entry.
* **Machine learning:** IKARUS uses machine learning techniques to learn from user interactions and to improve its performance over time. This makes IKARUS more intelligent and adaptive, and allows it to better meet the needs of its users.

IKARUS provides a number of features that make it a powerful and versatile knowledge management system, including:
* **Knowledge search and retrieval:** IKARUS provides a powerful knowledge search and retrieval engine that allows users to find the information they need quickly and easily. Users can search for knowledge using a variety of criteria, including keywords, semantic concepts, and relationships between concepts.
* **Knowledge reasoning and inference:** IKARUS can reason about knowledge and generate new knowledge based on existing knowledge. This makes it possible for IKARUS to answer complex questions that would be difficult or impossible to answer using traditional search engines.
* **Knowledge sharing and collaboration:** IKARUS allows users to share knowledge and collaborate with each other. Users can create and share knowledge bases, and they can work together to solve problems and make decisions.

IKARUS is a powerful and versatile knowledge management system that can be used in a wide variety of domains. It is still under development, but it has the potential to revolutionize the way that we acquire, manage, and use knowledge.

Here are some potential applications of IKARUS:
* **Business:** IKARUS can be used to manage customer knowledge, product knowledge, and market knowledge. It can also be used to develop expert systems and decision support systems.
* **Education:** IKARUS can be used to create and manage knowledge bases for educational resources, such as textbooks, articles, and videos. It can also be used to develop intelligent tutoring systems and adaptive learning systems.
* **Healthcare:** IKARUS can be used to manage patient knowledge, medical knowledge, and drug knowledge. It can also be used to develop expert systems for medical diagnosis and treatment planning.
* **Government:** IKARUS can be used to manage citizen knowledge, policy knowledge, and law knowledge. It can also be used to develop expert systems for public policy analysis and decision making.

IKARUS is a powerful tool that has the potential to revolutionize the way that we acquire, manage, and use knowledge. It is still under development, but it has the potential to have a significant impact on a wide range of industries and sectors."
[{2023-10-28 retrieved} https://bard.google.com/chat/eae955702a49c781]

name::
* McsEngl.IKARUS-Knlgmngr,
* McsEngl.Knlgmngr.010-IKARUS,
* McsEngl.Knlgmngr.IKARUS,
* McsEngl.klmrIkarus!=IKARUS--knowledge-manager,

descriptionLong::
"evolution of IKARUS-KRS:
IKARUS-KRS is an AI system developed by the German Aerospace Center (DLR). It is a knowledge-based reasoning system that is designed to solve complex problems in the areas of aerospace, robotics, and artificial intelligence.

**IKARUS-KRS's early years**
IKARUS-KRS was first introduced in the early 2000s. It was developed in response to the need for an AI system that could reason about complex systems and make decisions in real time.
IKARUS-KRS is based on a number of different AI technologies, including:
* Knowledge representation: IKARUS-KRS uses a knowledge representation language called KIF to store its knowledge. KIF is a powerful language that allows IKARUS-KRS to represent a wide range of different types of knowledge, including facts, rules, and relationships.
* Reasoning: IKARUS-KRS uses a variety of reasoning algorithms to solve problems. These algorithms include forward chaining, backward chaining, and abduction.
* Planning: IKARUS-KRS can also be used to plan and execute actions. It does this by using a planning algorithm called A*.

**IKARUS-KRS's evolution**
Since its introduction, IKARUS-KRS has evolved to become a more powerful and versatile AI system. Some of the key improvements that have been made to IKARUS-KRS include:
* The addition of new knowledge representation languages: In addition to KIF, IKARUS-KRS now also supports other knowledge representation languages, such as OWL and SWRL. This makes it easier to integrate IKARUS-KRS with other AI systems.
* The development of new reasoning algorithms: IKARUS-KRS now includes a number of new reasoning algorithms, such as non-monotonic reasoning and probabilistic reasoning. These algorithms allow IKRUS-KRS to reason about more complex problems.
* The improvement of planning capabilities: IKARUS-KRS's planning capabilities have also been improved. It can now plan for more complex tasks and it can also take into account uncertainty in the environment.

**IKARUS-KRS today**
Today, IKARUS-KRS is used in a variety of different applications, including:
* Aerospace: IKARUS-KRS is used to support the development of new aerospace systems, such as aircraft and spacecraft. It is also used to monitor and control aerospace systems during operation.
* Robotics: IKARUS-KRS is used to develop and control robots. It is also used to plan and execute robot missions.
* Artificial intelligence: IKARUS-KRS is used to develop new AI algorithms and systems. It is also used to evaluate the performance of AI systems.

**IKARUS-KRS's future**
DLR is continuing to invest in the development of IKARUS-KRS. Some of the areas that DLR is focusing on include:
* The development of new knowledge representation languages: DLR is working on developing new knowledge representation languages that are better suited for representing the knowledge that is needed to solve real-world problems.
* The development of new reasoning algorithms: DLR is also working on developing new reasoning algorithms that are more efficient and effective.
* The improvement of planning capabilities: DLR is working on improving IKARUS-KRS's planning capabilities so that it can plan for more complex tasks and take into account more uncertainty in the environment.

As AI continues to evolve, IKARUS-KRS is poised to play an increasingly important role in the development and deployment of new AI systems."
[{2023-10-30 retrieved} https://bard.google.com/chat/def7b1a45dc3d641]
===
"evolution of IKARUS-KMS:
IKARUS-KMS is a knowledge management system (KMS) developed by the Swiss Federal Institute of Technology in Zurich (ETH Zurich). It was originally released in 1999 and has since evolved to become one of the most powerful and versatile KMSs available.

**IKARUS-KMS in the early years**
The early versions of IKARUS-KMS focused on providing a central repository for storing and managing knowledge. It could be used to store a variety of different types of content, including documents, images, videos, and web pages. IKARUS-KMS also provided a number of features for organizing and searching knowledge, such as tagging, versioning, and full-text search.

**The evolution of IKARUS-KMS**
Over the years, IKARUS-KMS has evolved to become a more sophisticated and feature-rich KMS. Some of the key enhancements that have been made include:
* Support for collaborative knowledge management: IKARUS-KMS now supports a variety of features for collaborative knowledge management, such as discussion forums, wikis, and blogs. This allows users to work together to create, share, and manage knowledge.
* Integration with other systems: IKARUS-KMS can now be integrated with a variety of other systems, such as content management systems (CMSs), enterprise resource planning (ERP) systems, and customer relationship management (CRM) systems. This allows users to manage their knowledge in a single place and to access it from anywhere.
* Support for new content types: IKARUS-KMS now supports a wider range of content types, including social media posts, emails, and chat transcripts. This allows users to capture and manage all of their knowledge in one place.

**IKARUS-KMS today**
Today, IKARUS-KMS is used by a wide range of organizations, including universities, research institutes, government agencies, and businesses. It is a popular choice for organizations that need a powerful and versatile KMS to manage their knowledge assets.

**The future of IKARUS-KMS**
The developers of IKARUS-KMS are continuing to invest in the platform and to develop new features. Some of the areas that they are focusing on include:
* Support for artificial intelligence (AI): IKARUS-KMS is being integrated with AI technologies to make it more intelligent and user-friendly. For example, IKARUS-KMS can now use AI to recommend relevant content to users and to automate tasks such as knowledge extraction and classification.
* Support for the Semantic Web: IKARUS-KMS is being integrated with the Semantic Web to make it easier to share and link knowledge across different systems and organizations.
* Support for new devices and platforms: IKARUS-KMS is being developed to support a wider range of devices and platforms, including mobile devices and wearable devices.

As knowledge management becomes increasingly important for organizations, IKARUS-KMS is well-positioned to remain a leading KMS platform."
[{2023-10-30 retrieved} https://bard.google.com/chat/def7b1a45dc3d641]

{2021..2030} of techKnlg

description::
* {2020s}: AI is used to develop new products and services, such as self-driving cars and virtual assistants.
* {2023}:
- Douglas-Lenat, creator of Cyc, died. [https://en.wikipedia.org/wiki/Douglas_Lenat]
- GPT-4 announcement on 2023-03-14.
* {2022} ChatGPT was released in November 2022 and quickly gained popularity due to its ability to engage in open-ended, fluent conversations. It could generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way. [https://bard.google.com/]
* {2021} RDF-star: is an extension of RDF that allows triples to be nested within other triples.

name::
* McsEngl.techKnlg'{2030i10},
* McsEngl.{techInfo'2030i10}-techKnlg,

{2011..2020} of techKnlg

description::
* {2010s}: AI is widely adopted in a variety of industries.
* {2020}: OWL 2 WSDL Profile is introduced, enabling the integration of OWL ontologies into Web Services Description Language (WSDL).
* {2018-2019} Transfer Learning: particularly using pre-trained models like BERT for natural language understanding, became a prominent strategy for various NLP tasks. It significantly reduced the need for massive labeled datasets. [https://chat.openai.com/]
* {2017}:
- the Transformer architecture was introduced in large-language-models.
- SHACL Shapes Constraint Language: is a W3C Recommendation that provides a way to describe and validate the structure and constraints of RDF graphs.
* {2016} WebReason: A KRL that is based on modal logic and allows users to reason about counterfactuals and possible worlds.
* {2015} Deep Reinforcement Learning: The combination of deep learning with reinforcement learning led to significant progress in AI, with algorithms like AlphaGo beating world champions in board games. [https://chat.openai.com/]
* {2014} JSON-LD: JSON Linked Data is a JSON-based serialization for RDF data.
* {2012} RDFS++: A KRL that extends OWL with new features for representing temporal and spatial knowledge.

name::
* McsEngl.techKnlg'{2020i10},
* McsEngl.{techInfo'2020i10}-techKnlg,

{2001..2010} of techKnlg

description::
* {2000s}: Deep learning revolutionizes AI research.
* {2009}: OWL 2 DL becomes a W3C Recommendation, offering full logical reasoning capabilities.
* {2008} SPARQL: is a query language and protocol for querying RDF data.
* {2004}:
- OWL 1.0 becomes a W3C Recommendation, providing a foundation for representing rich semantics on the web.
- SWRL: A KRL that combines OWL with rules.

name::
* McsEngl.techKnlg'{2010i10},
* McsEngl.{techInfo'2010i10}-techKnlg,

{1991..2000} of techKnlg

description::
* {1990s}: Statistical learning gained prominence, and algorithms like Support Vector Machines (SVM) and decision trees became popular. The field also saw the emergence of ensemble methods. [https://chat.openai.com/]
* Development of knowledge reasoning systems for real-world applications such as fraud detection and customer relationship management.
* {2000}:
- DAML: DARPA Agent Markup Language, developed by the US Department of Defense.
- OIL: Ontology Inference Layer, developed by the European Union.
* {1999} RDF 1.0 was published as a W3C Recommendation in 1999.
* {1995}:
- Cycorp, Inc., based in Austin, Texas: Founded in January 1995 by AI pioneer Doug Lenat as a spin-off from MCC
- ONTOLINGUA: Public anouncement of the Ontolingua-Server.

name::
* McsEngl.techKnlg'{2000i10},
* McsEngl.{techInfo'2000i10}-techKnlg,

{1981..1990} of techKnlg

description::
* {1980s}: Machine learning becomes a popular subfield of AI.
* The expert systems of the early 1980s proved to be difficult to bulid because of the challenge of capturing all of an expert's knowledge. They were also difficult to maintain, because their large rule bases had little organization. Most expert systems were stand-alone applications on dedicated workstations. [BYTE, JUL 1993, 107]
* {1987} LOOM: One of the first ontological KRLs.
* {1985} KL-ONE: Another early ontological KRL.
* {1984} Cyc: Lenat initiates the Cyc project at MCC.
* {1982} Doug Lenat: Publishes the Knowledge Based Systems in Artificial Intelligence (1982, McGraw-Hill)

name::
* McsEngl.techKnlg'{1990i10},
* McsEngl.{techInfo'1990i10}-techKnlg,

{1971..1980} of techKnlg

description::
* In the 1970s, researchers began to develop knowledge-based systems (KBSs). KBSs are AI systems that use explicit knowledge to solve problems. This knowledge is represented in a variety of ways, such as logic rules, frames, and semantic nets. [https://bard.google.com/]
* Development of knowledge representation systems such as KL-ONE and PROLOG.
* {1972-1976} MYCIN is notable example of early expert systems.

name::
* McsEngl.techKnlg'{1980i10},
* McsEngl.{techInfo'1980i10}-techKnlg,

{1961..1970} of techKnlg

description::
* Development of first-order logic as a formal language for representing and reasoning about knowledge.
* {1966} ELIZA: A language for representing and reasoning about human natural language.
* {1965} Dendral is notable example of early expert systems.

name::
* McsEngl.techKnlg'{1970i10},
* McsEngl.{techInfo'1970i10}-techKnlg,

{1951..1960} of techKnlg

description::
* {1959} General Problem Solver (GPS) system developed by Allen Newell and Herbert A. Simon.
* {1957} perceptron: by Frank Rosenblatt, an American psychologist, was a simplified model of a neuron, capable of learning and recognizing patterns. [https://bard.google.com/]
* {1956}: John McCarthy coins the term "artificial intelligence".

name::
* McsEngl.techKnlg'{1960i10},
* McsEngl.{techInfo'1960i10}-techKnlg,

{1941..1950} of techKnlg

description::
* {1950}: Alan Turing proposes the Turing test, a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

name::
* McsEngl.techKnlg'{1950i10},
* McsEngl.{techInfo'1950i10}-techKnlg,

concept-mining of lagLgkl

description::
"overview of concept mining:
**Concept mining** is the process of extracting concepts from unstructured data, such as text, images, and audio. It is a subfield of knowledge discovery in databases (KDD) and text mining, and it uses a variety of techniques from artificial intelligence and statistics to identify and extract meaningful concepts from large datasets.

Concepts are abstract representations of real-world entities and ideas. They can be simple, such as "dog" or "house," or more complex, such as "democracy" or "artificial intelligence." Concepts can also be related to each other in various ways, such as through hypernymy (is-a relationship), meronymy (part-of relationship), or synonymy (similar meaning).

Concept mining is a powerful tool for discovering hidden knowledge in large datasets. It can be used for a variety of tasks, such as:
* **Topic modeling:** Identifying the main topics or themes in a collection of documents
* **Ontology construction:** Building knowledge bases that represent the relationships between concepts
* **Information retrieval:** Improving the accuracy and efficiency of search engines
* **Natural language processing:** Developing systems that can understand and generate human language
* **Machine learning:** Improving the performance of machine learning algorithms by providing them with better representations of the data

Concept mining is used in a wide range of industries, including healthcare, finance, marketing, and education. For example, concept mining can be used to identify the most common diseases and treatments in a medical database, or to discover the most popular products and brands in a social media dataset.

Here is a high-level overview of the concept mining process:
1. **Preprocessing:** The data is cleaned and prepared for analysis. This may involve removing noise, correcting errors, and stemming and lemmatizing words.
2. **Feature extraction:** Features are extracted from the data that are relevant to the concept mining task. For example, in text mining, features may include words, phrases, and syntactic patterns.
3. **Concept representation:** The features are used to represent the concepts in a way that is suitable for the concept mining algorithm. This may involve creating vectors of features or using a graph-based representation.
4. **Concept mining:** A concept mining algorithm is applied to the data to extract the concepts. There are a variety of different concept mining algorithms available, each with its own strengths and weaknesses.
5. **Concept evaluation:** The extracted concepts are evaluated to ensure that they are meaningful and accurate. This may involve using human experts to review the concepts or using automated evaluation metrics.

Concept mining is a complex and challenging task, but it has the potential to provide valuable insights into large datasets. As new concept mining algorithms and techniques are developed, it is likely to become an increasingly important tool for a wide range of applications."
[{2023-10-27 retrieved} https://bard.google.com/chat/65aecaa8a9ec5dac]

name::
* McsEngl.concept-mining//lagLgkl,
* McsEngl.lagLgkl'concept-mining,

semantic-parsing of lagLgkl

description::
"Semantic parsing is the task of converting a natural language utterance to a logical form: a machine-understandable representation of its meaning.[1] Semantic parsing can thus be understood as extracting the precise meaning of an utterance. Applications of semantic parsing include machine translation,[2] question answering,[1][3] ontology induction,[4] automated reasoning,[5] and code generation.[6][7] The phrase was first used in the 1970s by Yorick Wilks as the basis for machine translation programs working with only semantic representations.[8]"
[{2023-04-02 retrieved} https://en.wikipedia.org/wiki/Semantic_parsing]

name::
* McsEngl.lagLgkl'semantic-parsing,
* McsEngl.semantic-parsing,

manager of lagLgkl

description::
· logo-knowledge-language--manager is a-knowledge-language--manager that uses a-logo--knowledge-language-(logo-to--machine-meaning) to build its knowledge-base.

name::
* McsEngl.Knlgmngr.029-logo!⇒Lgklmngr,
* McsEngl.Knlgmngr.logo!⇒Lgklmngr,
* McsEngl.Lgklmngr!=Logo-Knowledge-Language--Manager,
* McsEngl.lagLgkl'manager!⇒Lgklmngr,
* McsEngl.logo-knowledge-language--manager!=Lgklmngr,

GENERIC-SPECIFIC-TREE of lagLgkl

generic-tree-of-lagLgkl::
* knowledge-language,
* ... entity,
* McsEngl.lagLgkl'generic-tree,

specific-tree-of-lagLgkl::
* controlled-natural-language,
* language-model,
* logic,
* propositional,

* McsEngl.lagLgkl.specific-tree,

lagLgkl.Abstract-Meaning-Representation

description::
"Abstract Meaning Representation (AMR)[1][2] is a semantic representation language. AMR graphs are rooted, labeled, directed, acyclic graphs (DAGs), comprising whole sentences. They are intended to abstract away from syntactic representations, in the sense that sentences which are similar in meaning should be assigned the same AMR, even if they are not identically worded. By nature, the AMR language is biased towards English – it is not meant to function as an international auxiliary language.
Abstract Meaning Representations have originally been introduced by Langkilde and Knight (1998)[3] as a derivation from the Penman Sentence Plan Language,[4] they are thus continuing a long tradition in Natural Language Generation and this has been their original domain of application. AMRs have re-gained attention since Banarescu et al. (2013),[1] in particular, this includes the extension to novel tasks such as machine translation and natural language understanding. The modern (post-2010) AMR format preserves the syntax and many syntactic conceptions of the original AMR format but has been thoroughly revised to better align with PropBank. Moreover, AMR has been extended with formal conventions for metadata and conventions for entity linking (here, linking with Wikipedia entries).
Existing AMR technology includes tools and libraries for parsing,[5] visualization,[6] and surface generation[7] as well as a considerable number of publicly available data sets. Many of these resources are collected at the AMR homepage[8] at ISI/USC where AMR technology has been originally developed."
[{2023-04-02 retrieved} https://en.wikipedia.org/wiki/Abstract_Meaning_Representation]

name::
* McsEngl.AMR!=Abstract-Meaning-Representation-lagLgkl,
* McsEngl.lagLgkl.Abstract-Meaning-Representation,

lagLgkl.Conceptual-Dependency-Theory

description::
"overview of Conceptual Dependency Theory:
Conceptual Dependency Theory is a knowledge representation framework developed by Roger C. Schank and Robert P. Abelson in the 1970s. This theory aims to model human cognition and understanding of natural language by representing knowledge in the form of conceptual structures called "conceptual dependencies." It is one of the early approaches to knowledge representation in the field of artificial intelligence and cognitive science. Here's an overview of Conceptual Dependency Theory:

1. **Basic Idea:** Conceptual Dependency Theory posits that human understanding and language comprehension can be represented as a set of fundamental, abstract concepts and their relationships. These abstract concepts serve as building blocks for constructing more complex representations of knowledge and understanding.

2. **Conceptual Dependencies:** The central elements of this theory are conceptual dependencies, which are used to represent the meaning of a sentence or a situation. A conceptual dependency consists of several components:
- **Action:** An action is a central concept that describes what is happening or being described in a sentence, such as "eat" or "give."
- **Object:** Objects are entities or things involved in the action, such as "John" or "apple."
- **Attribute:** Attributes provide additional information about an object, such as "red" for "apple."
- **Modifiers:** Modifiers are used to specify conditions or circumstances related to the action or object, such as "quickly" for "eat."
- **Time:** Time elements provide information about when the action occurs, like "yesterday" or "now."

3. **Conceptual Dependency Grammar:** Conceptual Dependency Theory uses a specialized grammar to represent sentences in terms of these conceptual dependencies. This grammar allows for the transformation of natural language sentences into a structured, abstract representation that captures the underlying meaning.

4. **Inference and Reasoning:** The theory includes mechanisms for performing inference and reasoning. It allows for the recognition of relationships, the generation of implications, and the understanding of context.

5. **Limitations:** Conceptual Dependency Theory, while innovative, has some limitations. It can be challenging to represent certain nuances of meaning and complex relationships, and it doesn't always scale well to more extensive knowledge domains. As a result, other knowledge representation models and approaches have gained prominence in artificial intelligence, such as frame-based systems and ontologies.

6. **Influence:** Despite its limitations, Conceptual Dependency Theory had a significant influence on the development of natural language processing and early AI systems. It contributed to the exploration of knowledge representation and understanding in the field.

7. **Evolution:** Over time, Conceptual Dependency Theory has evolved and has been incorporated into various other cognitive and AI models. Elements of it can still be seen in some modern approaches to knowledge representation and natural language understanding.

In summary, Conceptual Dependency Theory is a theory of knowledge representation that seeks to capture the essence of human cognition and language comprehension through abstract conceptual structures. While it is no longer the dominant model for AI knowledge representation, it has played a foundational role in the history of artificial intelligence and cognitive science."
[{2023-10-24 retrieved} https://chat.openai.com/c/5596bf85-a830-4ee1-9a2c-5c5a1eb3c481]

name::
* McsEngl.Conceptual-Dependency-Theory,
* McsEngl.lagLgkl.Conceptual-Dependency-Theory,

lagLgkl.embeddings

description::
"overview of embeddings:
Embeddings are a mathematical representation of data in a lower-dimensional space that preserves the underlying relationships between the data points. Embeddings are often used to represent complex data types, such as images, text, or audio, in a way that machine learning algorithms can easily process.

Embeddings are learned from data, and the specific learning algorithm used will depend on the type of data being embedded. For example, word embeddings are learned from text data, while image embeddings are learned from image data.

Once embeddings have been learned, they can be used for a variety of machine learning tasks. For example, word embeddings can be used for tasks such as sentiment analysis, text classification, and machine translation. Image embeddings can be used for tasks such as image classification and object detection.

Here are some of the benefits of using embeddings:
* **Reduced dimensionality:** Embeddings can be used to reduce the dimensionality of data, which can make machine learning algorithms more efficient and less prone to overfitting.
* **Improved performance:** Embeddings have been shown to improve the performance of machine learning algorithms on a variety of tasks.
* **Interpretability:** Embeddings can be used to visualize the relationships between data points, which can help to interpret the results of machine learning models.

Here are some examples of how embeddings are used in practice:
* **Search engines:** Search engines use word embeddings to match search queries to relevant documents.
* **Social media:** Social media platforms use word embeddings to recommend content to users and to identify abusive content.
* **Recommender systems:** Recommender systems use word embeddings to recommend products or services to users based on their past behavior.
* **Machine translation:** Machine translation systems use word embeddings to translate text from one language to another.

Embeddings are a powerful tool that can be used to improve the performance of machine learning algorithms on a variety of tasks. As machine learning continues to develop, embeddings are likely to play an even more important role in the field.
Sources: encord.com/blog/embeddings-machine-learning/"
[{2023-10-24 retrieved} https://bard.google.com/chat/1a0560b1ec4fbe96]

name::
* McsEngl.embeddings,
* McsEngl.lagLgkl.embeddings,

info-resource of lagCdnl

description::
* Controlled Natural Languages for Knowledge Representation, Rolf Schwitter, Centre for Language Technology, Macquarie University, Rolf.Schwitter@mq.edu.au, http://web.science.mq.edu.au/~rolfs/papers/coling10-schwitter.pdf,

name::
* McsEngl.lagCdnl'Infrsc,

lagCdnl.SPECIFIC

description::
* ACE'(attempto-controlled-English)-lagCdnl,
* CLCE'(common-logic-controlled-English)-lagCdnl,
* CLOnE-lagCdnl,
* CPL'(computer-processable-language)-lagCdnl,
* Formalized-English-lagCdnl,
* Lite-Natural-Language-lagCdnl,
* PENG'(processable-English)-lagCdnl,
* Rabbit-lagCdnl,
* SOS'(Sydney-OWL-Syntax)-lagCdnl,

name::
* McsEngl.lagCdnl.specific,

lagCdnl.ACE

description::
"overview of attempto-controlled-English:
Attempto Controlled English (ACE) is a controlled natural language (CNL) that is designed to bridge the gap between natural language and formal languages. It is a restricted form of English that allows for precise, unambiguous representation of information, making it suitable for various applications in fields such as computer science, artificial intelligence, and knowledge representation.

Here's an overview of Attempto Controlled English:
1. **Purpose**: ACE is primarily used for knowledge representation and natural language processing. It provides a way to express complex concepts and formal knowledge using a simplified subset of English, making it accessible to both humans and computers.

2. **Simplicity**: ACE is designed to be easily understandable by people with a basic understanding of English. It uses simplified grammar and vocabulary to reduce ambiguity and enhance precision.

3. **Formality**: Despite its simplicity, ACE retains the formality necessary for machine processing. It enforces strict rules for sentence structure and word usage to ensure clarity.

4. **Ambiguity Reduction**: One of the primary goals of ACE is to eliminate ambiguities that are inherent in standard English. This is achieved through structured sentences and controlled vocabulary.

5. **Applications**: ACE has found applications in various domains, including natural language interfaces for databases, knowledge representation, specification of business rules, and communication with intelligent systems.

6. **Semantics**: ACE is equipped with a formal semantics that enables precise translation into formal logic, which can then be processed by computers for various applications. This formalization is crucial for computational analysis.

7. **ACE Editor**: There is an ACE Editor tool that assists users in writing and validating ACE sentences. It helps ensure that the input conforms to the ACE grammar rules.

8. **Translation**: ACE can be translated into various formal representations, such as first-order logic or semantic web languages like OWL (Web Ontology Language), making it a valuable tool for converting natural language knowledge into machine-understandable formats.

9. **Developed by Attempto Project**: ACE was developed as part of the Attempto Project at the University of Zurich and is continually being developed and refined.

10. **Natural Language Interface**: One of the significant applications of ACE is providing natural language interfaces to databases and knowledge bases. Users can query structured data using English-like sentences.

In summary, Attempto Controlled English (ACE) is a restricted form of English that balances the need for human understandability with machine processability. It has applications in various fields where precise representation of knowledge is essential, such as AI, databases, and knowledge representation systems."
[{2023-10-23 retrieved} https://chat.openai.com/c/d19c460e-33cf-48e4-b88b-6be822f0498c]

name::
* McsEngl.ACE!=attempto-controlled-English!⇒lagAcen,
* McsEngl.attempto-controlled-English!⇒lagAcen,
* McsEngl.lagAcen,
* McsEngl.lagAcen!=Attempto-Controlled-ENglish,
* McsEngl.lagCdnl.Ace!⇒lagAcen,

descriptionLong::
"ACE (Fuchs et al., 2008) is a CNL that covers a well-defined subset of English that can be translated unambiguously into first-order logic via discourse representation structures (Kamp and Reyle, 1993) and then be used for automated reasoning. ACE is defined by a small set of construction rules that describe its syntax and a small set of interpretation rules that disambiguate constructs that might appear ambiguous in full English. The vocabulary of ACE consists of predefined function words (e.g. determiners, conjunctions, and pronouns), some predefined fixed phrases (e.g. there is, it is false that), and content words (nouns, proper names, verbs, adjectives, and adverbs). ACE supports language constructs such as:
• active and passive verbs (and modal verbs);
• strong negation (e.g. no, does not) and weak negation (e.g. is is not provable that);
• subject and object relative clauses;
• declarative, interrogative, imperative and conditional sentences;
• various forms of anaphoric references to noun phrases (e.g. he, himself, the man, X).
It is important to note that the meaning of words in ACE is not predefined; the user is expected to define their meaning by ACE sentences or import these definitions from an existing formal ontology.
Here is a simple example of an ACE text together with a question:
Every company that buys at least three machines gets a discount. Six Swiss companies each buy one machine. A German company buys four machines. Who gets a discount?
Note that ACE uses disambiguation markers (e.g. each) on the surface level and mathematical background knowledge about natural numbers in order to answer the question above. This mathematical knowledge is implemented as a set of Prolog predicates which are executed during the proof (question answering process).
ACE is supported by various tools[2], among them a text editor that helps users to construct correct ACE sentences with the help of hints and error messages, a parser that translates ACE texts into discourse representation structures, a paraphraser that reflects the interpretation of the machine in CNL, and a Satchmo-style reasoning engine that can be used for consistency and redundancy checking as well as for question answering. Applications of ACE include software and hardware specifications, agent control, legal and medical regulations, and ontology construction.
[2] http://attempto.ifi.uzh.ch/site/tools/"
[{2021-01-28} http://web.science.mq.edu.au/~rolfs/papers/coling10-schwitter.pdf]

lagCdnl.ClearTalk

description::
"ClearTalk is a controlled natural language—a kind of a formal language for expressing information that is designed to be both human-readable (being based on English) and easily processed by a computer.
Anyone who can read English can immediately read ClearTalk, and the people who write ClearTalk learn to write it while using it. The ClearTalk system itself does most of the training through use: the restrictions are shown by menus and templates and are enforced by immediate syntactic checks. By consistently using ClearTalk for its output, a system reinforces the acceptable syntactic forms.[1]
It is used by the experimental knowledge management software Ikarus and by a knowledge base management system Fact Guru.[2]"
[{2021-01-30} https://en.wikipedia.org/wiki/ClearTalk]

name::
* McsEngl.ClearTalk-lagCdnl,
* McsEngl.lagCdnl.ClearTalk,

input of lagPpsl

description::
· a-mind-view we want to map with the-propositional-language.

name::
* McsEngl.Ppslinput,
* McsEngl.lagPpsl'input!⇒Ppslinput,

output of lagPpsl

description::
· the-model that maps the-lagPpsl'input.

name::
* McsEngl.Ppsloutput,
* McsEngl.lagPpsl'output!⇒Ppsloutput,

evaluation of lagPpsl

description::
· the-inventors of propositional-languages do-not-understand that the-input is mind-concepts.
· that is why we see expressions like that they represent "entities, concepts, objects, attributes, properties" of the-real-world (domain of discourse).
===
· "the-first" airplanes had movable wings.
· the-same, propositional-languages model the-sentences of human-languages which in-turn are models of conceptual-views.
· we need to model directly the-conceptual-views. [hmnSngo.{2021-02-13}]

name::
* McsEngl.lagPpsl'evaluation,

lagPpsl.SPECIFIC

description::
* first-order-logic-language,
* ontology-language,

name::
* McsEngl.lagPpsl.specific,

lagPpsl.wikidata

description::
· wikidata-language is a-propositional-language that manages the-data collaborativelly.

">Wikidata data-representation-language:
Wikidata uses a data model called Wikibase, which is a flexible and extensible data model that is designed to be able to represent a wide variety of information. Wikibase is based on the Resource Description Framework (RDF), which is a standard for representing information in a linked data format.

RDF is a graph-based data model that consists of three types of things:
* **Subjects:** Subjects are the things that statements are made about.
* **Predicates:** Predicates are the properties of subjects.
* **Objects:** Objects are the values of predicates.

For example, the following statement represents the fact that Barack Obama is the president of the United States:
* **Subject:** Barack Obama (Q7676)
* **Predicate:** is president of (P31)
* **Object:** United States (Q768)

Wikibase extends RDF with a number of features that make it more suitable for representing the type of information that is stored in Wikidata. These features include:
* **Items:** Items are the entities that are represented in Wikidata. Items can be anything from people and places to events and concepts.
* **Properties:** Properties are the relationships between items. Properties are used to represent the various attributes of items.
* **Statements:** Statements are the assertions that are made about items. Statements are made up of subjects, predicates, and objects.
* **Data types:** Data types are used to specify the type of data that is stored in a property. Wikibase supports a variety of data types, including numbers, dates, and text strings.
* **Qualifiers:** Qualifiers are used to add additional information to statements. Qualifiers can be used to specify the time frame of a statement, the source of a statement, or other relevant information.

Wikibase is a powerful and flexible data model that is well-suited for representing the type of information that is stored in Wikidata. It is a key part of the Wikidata infrastructure, and it is used to store and retrieve data from the Wikidata database.

I hope this helps! Let me know if you have any other questions."
[{2023-11-18 retrieved} https://bard.google.com/chat/a20753ec7bb9ce20?hl=en&pli=1]

name::
* McsEngl.lagKnlg.008-wikidata!⇒lagWkdt,
* McsEngl.lagPpsl.wikidata!⇒lagWkdt,
* McsEngl.lagWkdt!=wikidata-lagPpsl,
* McsEngl.wikidata-lagPpsl!⇒lagWkdt,

descriptionLong::
"Wikidata is the free knowledge base that anyone can edit. Just like Wikipedia, Wikidata is built on the MediaWiki wiki package which means that content on pages (just like this one!) can be added, modified, or deleted in collaboration with others. Unlike Wikipedia, Wikidata also uses Wikibase software which allows for collaborative editing of structured data."
[{2021-01-25} https://www.wikidata.org/wiki/Help:Items]
===
"Wikidata is a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia, Wikimedia Commons, the other wikis of the Wikimedia movement, and to anyone in the world."
[{2021-01-25} https://www.wikidata.org/wiki/Wikidata:Introduction]

lagPpsl.entity-attribute-value-model

description::
"Entity–attribute–value model (EAV) is a data model to encode, in a space-efficient manner, entities where the number of attributes (properties, parameters) that can be used to describe them is potentially vast, but the number that will actually apply to a given entity is relatively modest. Such entities correspond to the mathematical notion of a sparse matrix.
EAV is also known as object–attribute–value model, vertical database model, and open schema."
[{2021-01-24} https://en.wikipedia.org/wiki/Entity%E2%80%93attribute%E2%80%93value_model]

name::
* McsEngl.lagKnlg.007-entity-attribute-value-model,
* McsEngl.lagPpsl.entity-attribute-value-model,

lagPpsl.attribute-value-system

description::
"An attribute-value system is a basic knowledge representation framework comprising a table with columns designating "attributes" (also known as "properties", "predicates", "features", "dimensions", "characteristics", "fields", "headers" or "independent variables" depending on the context) and "rows" designating "objects" (also known as "entities", "instances", "exemplars", "elements", "records" or "dependent variables").
Each table cell therefore designates the value (also known as "state") of a particular attribute of a particular object."
[{2021-01-24} https://en.wikipedia.org/wiki/Attribute-value_system]

name::
* McsEngl.lagKnlg.006-attribute-value-system,
* McsEngl.lagPpsl.attribute-value-system,

lagPpsl.Gellish

description::
"Information that is expressed in a Gellish formal language is computer interpretable and application system independent. It enables data exchange, data storage, data integration and systems interoperability without the need for data model harmonization or data conversion."
[{2021-01-29} http://wiki.gellish.net/start]

name::
* McsEngl.Gellish-language!⇒lagGlsh,
* McsEngl.lagKnlg.005-Gellish!⇒lagGlsh,
* McsEngl.lagGlsh,
* McsEngl.lagPpsl.Gellish!⇒lagGlsh,

descriptionLong::
"Gellish is an ontology language for data storage and communication, designed and developed by Andries van Renssen since mid-1990s.[1] It started out as an engineering modeling language ("Generic Engineering Language", giving it the name, "Gellish") but evolved into a universal and extendable conceptual data modeling language with general applications. Because it includes domain-specific terminology and definitions, it is also a semantic data modelling language and the Gellish modeling methodology is a member of the family of semantic modeling methodologies.
Although its concepts have 'names' and definitions in various natural languages, Gellish is a natural-language-independent formal language. Any natural language variant, such as Gellish Formal English is a controlled natural language. Information and knowledge can be expressed in such a way that it is computer-interpretable, as well as system-independent and natural language independent. Each natural language variant is a structured subset of that natural language and is suitable for information modeling and knowledge representation in that particular language. All expressions, concepts and individual things are represented in Gellish by (numeric) unique identifiers (Gellish UID's). This enables software to translate expressions from one formal natural language to any other formal natural languages."
[{2021-01-29} https://en.wikipedia.org/wiki/Gellish]

manager of lagLogic

description::
"overview of logic-based-system:
Logic-based systems (LBS) are artificial intelligence (AI) systems that use logic to reason about the world and make decisions. LBS are based on the idea that the world can be represented in terms of logical statements, and that these statements can be used to infer new knowledge and make decisions.

LBS are typically composed of two main components: a knowledge base and a reasoning engine. The knowledge base is a collection of logical statements that represent the system's knowledge about the world. The reasoning engine is a program that can use the knowledge base to infer new knowledge and make decisions.

LBS have a number of advantages over other types of AI systems. First, they are transparent and explainable. Unlike other AI systems, such as machine learning systems, LBS can explain the reasoning behind their decisions. This is because LBS are based on logic, which is a well-understood and well-studied field.

Second, LBS are robust. LBS are less likely to make mistakes than other types of AI systems, because they are based on sound logical principles.

Third, LBS are versatile. LBS can be used to solve a wide variety of problems, from simple tasks such as classification and prediction to more complex tasks such as planning and scheduling.

LBS are used in a wide variety of applications, including:
* **Expert systems:** Expert systems are LBS that are designed to solve specific problems in a particular domain, such as medical diagnosis or legal reasoning.
* **Planning systems:** Planning systems are LBS that are designed to generate plans for achieving specific goals.
* **Scheduling systems:** Scheduling systems are LBS that are designed to create and manage schedules for resources, such as people, machines, and equipment.
* **Natural language processing systems:** Natural language processing systems are LBS that are designed to understand and generate human language.
* **Knowledge representation systems:** Knowledge representation systems are LBS that are designed to represent and manage knowledge about the world.

LBS are a powerful and versatile tool for solving a wide variety of problems. However, they can be complex to develop and maintain. Additionally, LBS can be slow to run, especially for large and complex problems.

Here are some examples of logic-based systems:
* **Expert systems:** MYCIN, CLIPS, PROSPECTOR
* **Planning systems:** STRIPS, SHOP2, OPTAPLAN
* **Scheduling systems:** ISIS, ASPEN, ILOG Solver
* **Natural language processing systems:** Prolog, CYC, SHRDLU
* **Knowledge representation systems:** OWL, RDF, DAML+OIL

LBS is a rapidly growing field of research, and new LBS are being developed all the time. As LBS become more powerful and efficient, they are likely to play an increasingly important role in our lives."
[{2023-11-02 retrieved} https://bard.google.com/chat/525e647a5e1df871]

name::
* McsEngl.Knlgmngr.030-logic!⇒klmrLogic,
* McsEngl.Knlgmngr.logic!⇒klmrLogic,
* McsEngl.LBS!=logic-based-systems!⇒klmrLogic,
* McsEngl.klmrLogic!=logic--knowledge-language-manager,
* McsEngl.lagLogic'manager!⇒klmrLogic,
* McsEngl.logic-based-system!⇒klmrLogic,
* McsEngl.logic-knowledge-language--manager!⇒klmrLogic,

relation-to-natural-language of lagLogic

description::
"Natural languages are highly expressive systems that can state anything that has ever been stated in any formal language or logic.
They can even express metalevel statements about themselves, their relationships to other languages, and the truth of any such statements.
Such enormous expressive power can easily generate contradictions and paradoxes, such as the statement This sentence is false.
Most formal languages avoid such paradoxes by imposing restrictions on the expressive power."
[{2021-02-07} http://www.jfsowa.com/cg/cg_hbook.pdf]

name::
* McsEngl.lagHmnm'relation-to-lagLogic,
* McsEngl.lagLogic'relation-to-natural-language,

evoluting of lagLogic

description::
"evolution of logic-programing:
draft2
Logic programming is a programming paradigm that is based on formal logic. It is used to write programs by describing relationships between facts and rules. Logic programs are typically declarative, meaning that they specify what should be computed, but not how to compute it. This makes logic programming languages well-suited for expressing complex knowledge and reasoning problems.

The evolution of logic programming can be traced back to the late 1960s and early 1970s, when researchers at Stanford and Edinburgh Universities began developing declarative programming languages based on first-order logic. The most well-known logic programming language is Prolog, which was first developed in 1972.

Logic programming gained international attention during the 1980s, when it was chosen by the Japanese Ministry of International Trade and Industry to develop the software for the Fifth Generation Computer Systems (FGCS) project. The FGCS project aimed to use logic programming to develop advanced Artificial Intelligence applications on massively parallel computers. Although the project ultimately failed, it helped to raise awareness of logic programming and led to the development of new logic programming languages and tools.

In recent years, logic programming has continued to evolve, with new languages and techniques being developed for a variety of applications. For example, answer set programming (ASP) is a logic programming paradigm that is well-suited for solving combinatorial search problems. ASP has been used to develop applications in a variety of domains, including planning, scheduling, and diagnosis.

Another recent development in logic programming is the field of inductive logic programming (ILP). ILP is a machine learning technique that can be used to learn logic programs from data. ILP has been used to develop applications in a variety of domains, including knowledge discovery, natural language processing, and medical diagnosis.

Overall, logic programming has evolved from a niche language for artificial intelligence research to a powerful tool for developing a wide variety of applications. Logic programming languages are now used in a variety of industries, including healthcare, finance, and telecommunications.

Here is a timeline of some of the key events in the evolution of logic programming:
* **1972:** Prolog is first developed at the University of Edinburgh.
* **1981:** Prolog is standardized by the American National Standards Institute (ANSI).
* **1983:** The Fifth Generation Computer Systems (FGCS) project is launched in Japan.
* **1986:** The first conference on logic programming is held in Marseille, France.
* **1988:** The first book on answer set programming is published.
* **1993:** The first inductive logic programming system is developed.
* **1997:** The first book on inductive logic programming is published.
* **2007:** The first conference on answer set programming is held in Potsdam, Germany.
* **2010:** The first book on constraint logic programming is published.
* **2013:** The first international competition on inductive logic programming is held.

Today, logic programming is a thriving field of research and development. New logic programming languages and techniques are being developed for a variety of applications. Logic programming is also being used in conjunction with other programming paradigms, such as functional programming and object-oriented programming, to develop powerful and flexible software systems."
[{2023-11-04 retrieved} https://bard.google.com/chat/5f61651052df4528?hl=en&pli=1]

name::
* McsEngl.evoluting-of-lagLogic,
* McsEngl.lagLogic'evoluting,

lagLogic.SPECIFIC

description::
* Functional logic programming languages‎,
* Prolog programming language family‎,
* Absys,
* Algebraic Logic Functional programming language,
* Alice (programming language),
* Alma-0,
* CHIP (programming language),
* Ciao (programming language),
* CLACL,
* CycL,
* Datalog,
* Datomic,
* DLV,
* ECLiPSe,
* F-logic,
* Flix (programming language),
* Flora-2,
* Fril,
* FuzzyCLIPS,
* Game Description Language,
* Gφdel (programming language),
* HiLog,
* Janus (concurrent constraint programming language),
* ΛProlog,
* Logtalk,
* Maude system,
* Mercury (programming language),
* Oracle Policy Automation,
* Oz (programming language),
* Parlog,
* Planner (programming language),
* PROGOL,
* Prolog,
* Prolog++,
* Prolog32,
* Prova,
* .QL,
* Rebeca (programming language),
* ROOP (programming language),
* SWI-Prolog,
* ToonTalk,
* Transaction logic,
* Twelf,
* Visual Prolog,
* XSB,
[{2021-02-05} https://en.wikipedia.org/wiki/Category:Logic_programming_languages]

name::
* McsEngl.lagLogic.specific,

lagLogic.first-order-logic

description::
"first order logic overview
First-order logic (FOL), also known as first-order predicate logic or first-order logic with quantifiers, is a formal system used for representing and reasoning about statements and relationships in a wide range of domains. It provides a powerful and expressive language for expressing facts, making deductions, and conducting formal reasoning. Here's an overview of its key components and concepts:
* Symbols and Syntax:
- Variables: Symbols (e.g., x, y, z) that represent unspecified objects or elements.
- Constants: Symbols (e.g., a, b, c) denoting specific, constant objects in a domain.
- Functions: Symbols (e.g., f(x), g(y)) representing operations or functions that take one or more arguments.
- Predicates: Symbols (e.g., P(x), Q(x, y)) representing properties or relations between objects.
- Quantifiers: Universal quantifier (∀) and existential quantifier (∃) are used to specify the scope of variables in statements.
- Logical Connectives: Including conjunction (∧), disjunction (∨), implication (→), negation (¬), and equivalence (↔), used to combine and modify statements.
* Semantics:
- An interpretation defines the meaning of the symbols within a specific domain.
- Interpretations specify the domain of objects, assign meanings to constants and functions, and determine whether predicates are true or false for specific objects.
- Truth values of complex statements are derived based on the interpretations of their constituent parts.
* Quantifiers:
- Universal Quantifier (∀): Represents "for all" and asserts that a statement holds true for every element in the domain.
- Existential Quantifier (∃): Represents "there exists" and asserts that there is at least one element in the domain for which a statement is true.
* Inference Rules:
- FOL includes inference rules (e.g., modus ponens, universal instantiation, existential instantiation) that allow you to deduce new statements from existing ones.
- These rules ensure that deductions are logically valid.
* Axiomatic Systems and Proofs:
- FOL can be formalized using axiomatic systems like Hilbert-style or natural deduction.
- Proofs within FOL are constructed following well-defined rules and axioms.
* Completeness and Soundness:
- FOL is both complete and sound.
- Completeness: All logically valid statements can be proven within the system.
- Soundness: Any statement proven within the system is guaranteed to be logically valid.
* Applications:
- FOL is used in various fields, including mathematics, computer science, artificial intelligence, and philosophy.
- It serves as the foundation for formal knowledge representation, automated theorem proving, and reasoning in symbolic AI systems.
* Expressive Power:
- FOL is capable of expressing a wide range of statements and relationships, but it has limitations. For example, it cannot express statements about sets of sets (second-order logic is required for this).
In summary, first-order logic provides a precise and formal framework for expressing statements, making deductions, and conducting rigorous reasoning in diverse domains. Its expressive power and robust mathematical foundations make it a fundamental tool in logic and computational applications."
[{2023-09-17 retrieved} https://chat.openai.com/c/d3b15dcb-d779-4b1e-9621-ba290d591743]

name::
* McsEngl.FOL!=first-order-logic-language,
* McsEngl.first-order-logic-language!⇒lagFolc,
* McsEngl.first-order-predicate-calculus!⇒lagFolc,
* McsEngl.lagFrml.003-lagFolc,
* McsEngl.lagFrml.lagFolc,
* McsEngl.lagFolc,
* McsEngl.lagFolc!=first-order-logic-language,
* McsEngl.lagKnlg.015-first-order-logic!⇒lagFolc,
* McsEngl.lagKnlg.first-order-logic!⇒lagFolc,
* McsEngl.lagLogic.first-order-logic!⇒lagFolc,
* McsEngl.lagPpsl.first-order-logic!⇒lagFolc,
* McsEngl.predicate-logic!⇒lagFolc,
* McsEngl.quantificational-logic!⇒lagFolc,

descriptionLong::
"first order logic
First-order logic (FOL), also known as predicate logic or first-order predicate calculus, is a formal system of logic that is widely used in mathematics, philosophy, computer science, and linguistics. It is a more expressive language than propositional logic, which is a simpler form of logic that can only represent statements about propositions (which are either true or false). FOL can represent statements about objects and their properties, as well as the relationships between objects.
FOL is based on the following concepts:
* Objects: Objects are the basic building blocks of FOL. They can be anything, such as people, places, things, or ideas.
* Predicates: Predicates are properties of objects. For example, the predicate "is human" is a property of the object "Socrates".
* Functions: Functions are relationships between objects. For example, the function "father of" relates a father to his child.
* Quantifiers: Quantifiers allow us to make statements about all or some of the objects in a domain. For example, the quantifier "all" can be used to make the statement "All humans are mortal".
* Sentences: Sentences in FOL are statements about objects, predicates, functions, and quantifiers. They can be either true or false.
FOL can be used to express a wide variety of statements, such as:
* All humans are mortal.
* Socrates is a man.
* Every even number is divisible by two.
* There exists a prime number greater than 100.
* For all real numbers x and y, if x is less than y, then x + y is less than 2y.
FOL is a powerful tool for reasoning and problem-solving. It can be used to prove theorems, design algorithms, and develop intelligent systems.
Here are some examples of FOL statements:
* ∀x(Human(x) → Mortal(x)) (All humans are mortal.)
* Human(Socrates) (Socrates is a human.)
* Even(n) → DivisibleByTwo(n) (Every even number is divisible by two.)
* ∃x(Prime(x) ∧ x > 100) (There exists a prime number greater than 100.)
* ∀x∀y(x < y → x + y < 2y) (For all real numbers x and y, if x is less than y, then x + y is less than 2y.)
FOL is a complex topic, but it is an essential foundation for many areas of mathematics, computer science, and philosophy. If you are interested in learning more about FOL, there are many resources available online and in libraries."
[{2023-09-17 retrieved} https://bard.google.com/]
===
"First-order logic — also known as predicate logic, quantificational logic, and first-order predicate calculus — is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects, and allows the use of sentences that contain variables, so that rather than propositions such as "Socrates is a man", one can have expressions in the form "there exists x such that x is Socrates and x is a man", where "there exists" is a quantifier, while x is a variable.[1] This distinguishes it from propositional logic, which does not use quantifiers or relations;[2] in this sense, propositional logic is the foundation of first-order logic.
A theory about a topic is usually a first-order logic together with a specified domain of discourse (over which the quantified variables range), finitely many functions from that domain to itself, finitely many predicates defined on that domain, and a set of axioms believed to hold about them. Sometimes, "theory" is understood in a more formal sense, which is just a set of sentences in first-order logic.
The adjective "first-order" distinguishes first-order logic from higher-order logic, in which there are predicates having predicates or functions as arguments, or in which one or both of predicate quantifiers or function quantifiers are permitted.[3]:56 In first-order theories, predicates are often associated with sets. In interpreted higher-order theories, predicates may be interpreted as sets of sets.
There are many deductive systems for first-order logic which are both sound (i.e., all provable statements are true in all models) and complete (i.e. all statements which are true in all models are provable). Although the logical consequence relation is only semidecidable, much progress has been made in automated theorem proving in first-order logic. First-order logic also satisfies several metalogical theorems that make it amenable to analysis in proof theory, such as the Lφwenheim–Skolem theorem and the compactness theorem.
First-order logic is the standard for the formalization of mathematics into axioms, and is studied in the foundations of mathematics. Peano arithmetic and Zermelo–Fraenkel set theory are axiomatizations of number theory and set theory, respectively, into first-order logic. No first-order theory, however, has the strength to uniquely describe a structure with an infinite domain, such as the natural numbers or the real line. Axiom systems that do fully describe these two structures (that is, categorical axiom systems) can be obtained in stronger logics such as second-order logic.
The foundations of first-order logic were developed independently by Gottlob Frege and Charles Sanders Peirce.[4] For a history of first-order logic and how it came to dominate formal logic, see Josι Ferreirσs (2001)."
[{2021-01-24} https://en.wikipedia.org/wiki/First-order_logic]

lagLogic.higher-order-logic

description::
"First-order logic quantifies only variables that range over individuals; second-order logic, in addition, also quantifies over sets; third-order logic also quantifies over sets of sets, and so on.
Higher-order logic is the union of first-, second-, third-, …, nth-order logic; i.e., higher-order logic admits quantification over sets that are nested arbitrarily deeply."
[{2021-02-05} https://en.wikipedia.org/wiki/Higher-order_logic]

name::
* McsEngl.HOL!=higher-order-logic!⇒lagHolc,
* McsEngl.higher-order-logic!⇒lagHolc,
* McsEngl.lagKnlg.016-higher-order-logic!⇒lagHolc,
* McsEngl.lagKnlg.higher-order-logic!⇒lagHolc,
* McsEngl.lagHolc!=higher-order-logic,
* McsEngl.lagLogic.higher-order-logic!⇒lagHolc,

descriptionLong::
"In mathematics and logic, a higher-order logic is a form of predicate logic that is distinguished from first-order logic by additional quantifiers and, sometimes, stronger semantics. Higher-order logics with their standard semantics are more expressive, but their model-theoretic properties are less well-behaved than those of first-order logic.
The term "higher-order logic", abbreviated as HOL, is commonly used to mean higher-order simple predicate logic. Here "simple" indicates that the underlying type theory is the theory of simple types, also called the simple theory of types (see Type theory). Leon Chwistek and Frank P. Ramsey proposed this as a simplification of the complicated and clumsy ramified theory of types specified in the Principia Mathematica by Alfred North Whitehead and Bertrand Russell. Simple types is nowadays sometimes also meant to exclude polymorphic and dependent types.[1]"
[{2021-02-05} https://en.wikipedia.org/wiki/Higher-order_logic]

lagLogic.conceptual-graphs-{1976..}

description::
"Conceptual graphs (CGs) are a system of logic based on the existential graphs of Charles Sanders Peirce and the semantic networks of artificial intelligence.
They express meaning in a form that is logically precise, humanly readable, and computationally tractable.
With a direct mapping to language, conceptual graphs serve as an intermediate language for translating computer-oriented formalisms to and from natural languages.
With their graphic representation, they serve as a readable, but formal design and specification language.
CGs have been implemented in a variety of projects for information retrieval, database design, expert systems, and natural language processing."
[{2021-02-07} http://www.jfsowa.com/cg/-{2005-03-14}]

name::
* McsEngl.CGs!=conceptual-graphs!⇒lagCgaf,
* McsEngl.conceptual-graphs-abstract-form!⇒lagCgaf,
* McsEngl.lagKnlg.017-conceptual-graphs!⇒lagCgaf,
* McsEngl.lagKnlg.conceptual-graphs!⇒lagCgaf,
* McsEngl.lagCgaf!=conceptual-graphs-abstract-form,
* McsEngl.lagLogic.conceptual-graphs!⇒lagCgaf,

lagLogic.description-logic

description::
"Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy description logics, and each description logic features a different balance between expressive power and reasoning complexity by supporting different sets of mathematical constructors.[1]
DLs are used in artificial intelligence to describe and reason about the relevant concepts of an application domain (known as terminological knowledge). It is of particular importance in providing a logical formalism for ontologies and the Semantic Web: the Web Ontology Language (OWL) and its profile is based on DLs. The most notable application of DLs and OWL is in biomedical informatics where DL assists in the codification of biomedical knowledge."
[{2021-01-24} https://en.wikipedia.org/wiki/Description_logic]

name::
* McsEngl.DL!=description-logic-language,
* McsEngl.description-logic-language!⇒lagDlgc,
* McsEngl.lagDlgc!=description-logic-language,
* McsEngl.lagKnlg.018-description-logic!⇒lagDlgc,
* McsEngl.lagKnlg.description-logic!⇒lagDlgc,
* McsEngl.lagPpsl.description-logic!⇒lagDlgc,
* McsEngl.lagPpsl.description-logic!⇒lagDlgc,

descriptionLong::
"Description logic was designed as an extension to frames and semantic networks, which were not equipped with formal logic-based semantics".
[http://en.wikipedia.org/wiki/Description_logic]

lagLogic.frame-logic

description::
"The Frame Logic (or F-logic) provides a logical foundation for frame-based and object-oriented languages for data and knowledge representation."
[{2021-02-04} http://flora.sourceforge.net/aboutFlogic.html]

name::
* McsEngl.F-logic-language!⇒lagFlgc,
* McsEngl.frame-logic-language!⇒lagFlgc,
* McsEngl.lagKnlg.019-frame-logic!⇒lagFlgc,
* McsEngl.lagKnlg.frame-logic!⇒lagFlgc,
* McsEngl.lagFlgc,
* McsEngl.lagFlgc!=frame-logic-language,
* McsEngl.lagLogic.F-logic!⇒lagFlgc,

descriptionLong::
"F-logic [15] extends classical predicate calculus with the concepts of objects, classes, and types, which are adapted from object-oriented programming.
In this way, F-logic integrates the paradigms of logic programming and deductive databases with the object-oriented programming paradigm.
Most of the applications of F-logic have been as a language for intelligent information systems based on the logic programming paradigm. This was the original motivation for the development of F-logic. More recently, Flogic has been used to represent ontologies and other forms of Semantic Web reasoning [9, 8, 25, 1, 23, 14]."
[{2021-02-04} https://www3.cs.stonybrook.edu/~kifer/TechReports/ontologies-rules-flogic.pdf]
===
"F-logic (frame logic) is a knowledge representation and ontology language. F-logic combines the advantages of conceptual modeling with object-oriented, frame-based languages and offers a declarative, compact and simple syntax, as well as the well-defined semantics of a logic-based language.
Features include, among others, object identity, complex objects, inheritance, polymorphism, query methods, encapsulation. F-logic stands in the same relationship to object-oriented programming as classical predicate calculus stands to relational database programming."
[{2021-02-04} https://en.wikipedia.org/wiki/F-logic]

lagLogic.transaction-logic

description::
"Transaction Logic provides logical foundations for state changes and side effects in a logic programming language. A significant portion of this theory is implemented in Flora-2. Applications of Transaction Logic include modeling and reasoning about workflows, planning, robotics, view maintenance in databases, and more."
[{2021-02-04} http://flora.sourceforge.net/aboutTR.html]

name::
* McsEngl.TL!=transaction-logic!⇒lagTlgc,
* McsEngl.lagKnlg.020-transaction-logic!⇒lagTlgc,
* McsEngl.lagKnlg.transaction-logic!⇒lagTlgc,
* McsEngl.lagLogic.transaction-logic!⇒lagTlgc,
* McsEngl.lagTlgc!=transaction-logic-language,
* McsEngl.transaction-logic-language!⇒lagTlgc,

lagLogic.Datalog-{1977}

description::
">evolution of DataLog language:
The evolution of the Datalog language has been a fascinating journey, spanning over four decades of research and development. From its early beginnings as a simple declarative query language, Datalog has evolved into a powerful and versatile tool for a wide range of applications.

**Early Years (1970s-1980s)**
Datalog emerged in the 1970s as a subset of the logic programming language Prolog. It was designed to be a more database-oriented language, with a focus on efficiency and expressiveness. Datalog quickly gained popularity in the academic community, as it provided a clean and concise way to represent and query data.

**The Rise of Deductive Databases (1980s-1990s)**
In the 1980s, Datalog became the foundation for a new type of database system called deductive databases. Deductive databases combined the power of logic programming with the efficiency of traditional databases. This made them well-suited for a wide range of applications, including knowledge representation, reasoning, and rule-based systems.

**Extensions and Variants (1990s-present)**
As Datalog matured, it was extended with new features to support a wider range of applications. These extensions included support for negation, aggregate functions, and non-deterministic rules. Datalog also spawned a number of variants, each with its own unique set of features.

**Modern Datalog (2000s-present)**
Today, Datalog is a mature and widely used language with a rich ecosystem of tools and applications. It is used in a variety of domains, including knowledge management, data integration, and semantic web applications. Datalog is also being used in the development of new data science and machine learning algorithms.

**Future of Datalog**
The future of Datalog is bright. As the volume and complexity of data continues to grow, Datalog's declarative and expressive nature is becoming increasingly valuable. Datalog is well-positioned to play a leading role in the development of new data management and analysis tools.
Here are some of the key trends in the evolution of Datalog:
* **Increased expressiveness:** Datalog has been extended with new features that make it more expressive, such as negation and aggregate functions.
* **Improved performance:** Datalog engines have become more efficient, making them able to handle larger and more complex datasets.
* **New applications:** Datalog is being used in a wider range of applications, including knowledge management, data integration, and semantic web applications.

I hope this overview has been helpful. Please let me know if you have any other questions."
[{2023-11-08 retrieved} https://bard.google.com/chat/90cb1636d6b6adb7?hl=en&pli=1]

name::
* McsEngl.Datalog-lagLogic!⇒lagDtlg,
* McsEngl.lagDtlg!=Datalog--logic-language,
* McsEngl.lagKnlg.028-Datalog!⇒lagDtlg,
* McsEngl.lagKnlg.Datalog!⇒lagDtlg,
* McsEngl.lagLogic.Datalog!⇒lagDtlg,

lagLogic.Prolog-{1972}

description::
"Prolog is a logic programming language associated with artificial intelligence and computational linguistics."
[{2021-02-04} https://en.wikipedia.org/wiki/Prolog]

name::
* McsEngl.Prolog!⇒lagPrlg,
* McsEngl.lagKnlg.021-Prolog!⇒lagPrlg,
* McsEngl.lagKnlg.Prolog!⇒lagPrlg,
* McsEngl.lagLogic.Prolog!⇒lagPrlg,
* McsEngl.lagPrlg!=Prolog--logic-language,

descriptionLong::
"Prolog is a logic programming language associated with artificial intelligence and computational linguistics.[1][2][3]
Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily as a declarative programming language: the program logic is expressed in terms of relations, represented as facts and rules. A computation is initiated by running a query over these relations.[4]
The language was developed and implemented in Marseille, France, in 1972 by Alain Colmerauer with Philippe Roussel, based on Robert Kowalski's procedural interpretation of Horn clauses.[5][6]
Prolog was one of the first logic programming languages[7] and remains the most popular such language today, with several free and commercial implementations available. The language has been used for theorem proving,[8] expert systems,[9] term rewriting,[10] type systems,[11] and automated planning,[12] as well as its original intended field of use, natural language processing.[13][14] Modern Prolog environments support the creation of graphical user interfaces, as well as administrative and networked applications.
Prolog is well-suited for specific tasks that benefit from rule-based logical queries such as searching databases, voice control systems, and filling templates."
[{2021-02-04} https://en.wikipedia.org/wiki/Prolog]

lagLogic.HiLog-{1986..}

description::
"evolution of HiLog:
HiLog, or Higher-order Logic, was one of the first higher-order query languages. It was developed in the late 1980s by Michael Kifer and Loui Rundensteiner, and was based on the logic programming language Prolog. HiLog was designed to be a powerful and expressive query language for deductive databases.

HiLog has evolved over time to include new features and capabilities. Some of the key changes include:
* **Support for object-oriented databases:** HiLog was extended in the early 1990s to support object-oriented databases. This allowed HiLog users to query and manipulate data in object-oriented databases using a logical programming paradigm.
* **Support for cooperative query answering:** HiLog was extended in the mid-1990s to support cooperative query answering. This allowed HiLog users to query multiple databases in a cooperative and consistent manner.
* **Support for meta-programming:** HiLog was extended in the late 1990s to support meta-programming. This allowed HiLog users to write programs that manipulate and reason about other HiLog programs.

In addition to these major changes, HiLog has also been extended with a number of minor features and improvements over time.

HiLog has been used to develop a variety of database applications, including knowledge base systems, rule-based systems, and deductive database systems. HiLog is also used in a number of academic research projects on database languages and systems.

Here is a summary of the key evolution milestones of HiLog:
* **1986:** HiLog is introduced by Michael Kifer and Loui Rundensteiner.
* **1989:** HiLog is extended to support object-oriented databases.
* **1993:** HiLog is extended to support cooperative query answering.
* **1996:** HiLog is extended to support meta-programming.
* **2000-present:** HiLog continues to be extended with new features and improvements.

HiLog is a powerful and expressive query language that has been used to develop a variety of database applications. It is still under active development, and new features and improvements are being added all the time."
[{2023-11-04 retrieved} https://bard.google.com/chat/fc74e240b86fc557?hl=en&pli=1]

name::
* McsEngl.HiLog,
* McsEngl.lagLogic.HiLog,

lagLogic.functional-logic

description::
"Functional logic programming is the combination, in a single programming language, of the paradigms of functional programming and logic programming.[1] This style of programming is embodied by various programming languages, including Curry and Mercury.[2][1]
A journal devoted to the integration of functional and logic programming was published by MIT Press and the European Association for Programming Languages and Systems between 1995 and 2008.[3]"
[{2021-02-05} https://en.wikipedia.org/wiki/Functional_logic_programming]

name::
* McsEngl.FLP!=functional-logic-programming!⇒lagFnlc,
* McsEngl.lagKnlg.022-functional-logic!⇒lagFnlc,
* McsEngl.lagKnlg.functional-logic!⇒lagFnlc,
* McsEngl.lagFnlc!=functional-logic,
* McsEngl.lagLogic.functional-logic!⇒lagFnlc,

lagLogic.common-logic

description::
"Common Logic (CL) evolved from two projects to develop parallel ANSI standards for conceptual graphs and the Knowledge Interchange Format (Genesereth & Fikes 1992). Eventually, those projects were merged into a single ISO project to develop a common abstract syntax and model-theoretic foundation for a family of logic-based notations (ISO/IEC 24707). Hayes and Menzel (2001) defined a very general model theory for CL, which Hayes and McBride (2003) used to define the semantics for the languages RDF(S) and OWL. In addition to the abstract syntax and model theory, the CL standard specifies three concrete dialects that are capable of expressing the full CL semantics: the Common Logic Interchange Format (CLIF), the Conceptual Graph Interchange Format (CGIF), and the XML-based notation for CL (XCL). Since the semantics of RDF and OWL is based on a subset of CL semantics, those languages can also be considered dialects of CL: any statement in RDF or OWL can be translated to CLIF, CGIF, or XCL, but only a subset of CL can be translated back to RDF or OWL."
[{2021-02-05} http://www.jfsowa.com/cg/cgif.htm]

name::
* McsEngl.CL!=common-logic-language,
* McsEngl.common-logic-language!⇒lagCmlc,
* McsEngl.lagCmlc,
* McsEngl.lagCmlc!=common-logic,
* McsEngl.lagKnlg.023-common-logic!⇒lagCmlc,
* McsEngl.lagKnlg.common-logic!⇒lagCmlc,
* McsEngl.lagLogic.common-logic!⇒lagCmlc,

lagLogic.XSB-{1993-1998-2023..}

description::
"XSB is the name of a dialect of the Prolog programming language and its implementation developed at Stony Brook University in collaboration with the Katholieke Universiteit Leuven, the New University of Lisbon, Uppsala University and software vendor XSB, Inc.
XSB extends Prolog with tabled resolution and HiLog (a standard extension of Prolog permitting limited higher-order logic programming).
The open source XSB implementation includes an interface to the Java programming language."
[{2021-02-04} https://en.wikipedia.org/wiki/XSB]

name::
* McsEngl.XSB!⇒lagXsbl,
* McsEngl.lagKnlg.024-XSB!⇒lagXsbl,
* McsEngl.lagKnlg.XSB!⇒lagXsbl,
* McsEngl.lagLogic.XSB!⇒lagXsbl,
* McsEngl.lagXsbl,

descriptionLong::
"Since its inception, a primary goal of XSB has been to expand the areas in which Prolog is used, by making Prolog more powerful, more efficient, and more declarative. In 1993 when XSB was first released, it supported this goal by including both tabled resolution for definite programs, which provided it with deductive databasestyle features of such systems as Coral (Ramakrishnan et al. 1992) and LDL (Chimenti et al. 1990). At the time, while XSB was faster than those systems, it was basically suitable only for research by its developers. Since then, XSB has become a widely used multi-threaded Prolog that is compliant with most standards. During this development, XSB’s research focus has continued to be centered on tabling.
At one level, the idea behind tabling is simple; subgoals encountered in a query evaluation are maintained in a table, along with answers to these subgoals. If a subgoal is re-encountered, the evaluation reuses information from the table rather than re-performing resolution against program clauses.
[{2021-02-04} https://www3.cs.stonybrook.edu/~tswift/webpapers/tplp-10-sips.pdf]

lagLogic.Flora-2

description::
"ErgoAI is the new name for Ergo Suite. ErgoAI greatly extends the well-known open-source Flora-2 system (https://flora.sourceforge.net/) and, as of ErgoAI’s V3.0 release, Flora-2 as a distinct system is being retired."
[{2023-11-04 retrieved} http://coherentknowledge.com/]

"Flora-2 (a.k.a. Ergo Lite) is an advanced object-oriented knowledge representation and reasoning system. It is a dialect of F-logic with numerous extensions, including meta-programming in the style of HiLog, logical updates in the style of Transaction Logic, and defeasible reasoning. Applications include intelligent agents, Semantic Web, knowledge-based networking, ontology management, integration of information, security policy analysis, and more."
[{2021-02-04} http://flora.sourceforge.net/]

name::
* McsEngl.lagKnlg.025-Flora-2-language!⇒lagFlr2,
* McsEngl.lagKnlg.Flora-2-language!⇒lagFlr2,
* McsEngl.lagFlr2,
* McsEngl.lagFlr2!=Flora-2-language,
* McsEngl.lagLogic.Flora-2!⇒lagFlr2,

descriptionLong::
"Flora-2 is an open source semantic rule-based system for knowledge representation and reasoning. The language of the system is derived from F-logic,[1] HiLog,[2] and Transaction logic.[3] Being based on F-logic and HiLog implies that object-oriented syntax and higher-order representation are the major features of the system. Flora-2 also supports a form of defeasible reasoning called Logic Programming with Defaults and Argumentation Theories (LPDA).[4] Applications include intelligent agents, Semantic Web, knowledge-bases networking, ontology management, integration of information, security policy analysis, automated database normalization, and more.[5][6][7][8][9][10]
Flora-2 relies on the XSB system for its inference engine. The design and architecture of Flora-2 are described in a number of works.[11][12][13][14]
Details of the system and its use are described in the Flora-2 User's Manual.[15] Flora-2 is available for all major computing platforms, including Linux and other flavors of Unix, Microsoft Windows (both 32- and 64-bit), and Mac OS X."
[{2021-02-04} https://en.wikipedia.org/wiki/Flora-2]

tech of lagCycl

description::
· any tech used.

name::
* McsEngl.lagCycl'tech,

input of lagCycl

description::
· common sense knowledge.

name::
* McsEngl.lagCycl'input,

output of lagCycl

description::
·

name::
* McsEngl.lagCycl'output,

SubL of lagCycl

description::
"SubL is a computer language built by members of Cycorp.
SubL was written to support the Cyc® application, allowing it to run both under Lisp environments and as a C application generated by a SubL-to-C translator"
[{2021-02-09} http://web.archive.org/web/20160822085707/http://www.cyc.com/documentation/subl-reference/]
===
"SubL stands for SubLanguage, and is a computer language created by the CYC® team. It is designed to be easy to translate into C. The entire CYC® application is written in SubL.
Sublisp
An archaic term for SubL."
[{2021-02-09} http://web.archive.org/web/20121219042246/http://www.cyc.com:80/cycdoc/ref/glossary.html#SubL]

name::
* McsEngl.SubL!⇒lagSubl,
* McsEngl.SubLanguage!⇒lagSubl,
* McsEngl.Sublisp!⇒lagSubl,
* McsEngl.lagCycl'SubL!⇒lagSubl,
* McsEngl.lagSubl,

human of lagCycl

description::
· any human related to Cyc-project.

name::
* McsEngl.Cychmn,
* McsEngl.lagCycl'human!⇒Cychmn,

organization of lagCycl

description::
·

name::
* McsEngl.lagCycl'organization,

project of lagCycl

description::
· klmrCyc created as a-project at MCC.

name::
* McsEngl.Cycproject,
* McsEngl.lagCycl'project!⇒Cycproject,

evaluation of lagCycl

description::
· there is no ONE common sense knowledge.

name::
* McsEngl.lagCycl'evaluation,

info-resource of lagCycl

description::
* http://www.cyc.com/,
* http://web.archive.org/web/20121219042246/http://www.cyc.com:80/cycdoc/ref/glossary.html,

name::
* McsEngl.lagCycl'Infrsc,

evoluting of lagCycl

{2012}-OpenCyc-4.0::
"A version of OpenCyc, 4.0, was released in June 2012. OpenCyc 4.0 included much of the Cyc ontology at that time, containing hundreds of thousands of terms, along with millions of assertions relating the terms to each other; however, these are mainly taxonomic assertions, not the complex rules available in Cyc. The OpenCyc 4.0 knowledge base contained 239,000 concepts and 2,093,000 facts."
[{2021-02-08} https://en.wikipedia.org/wiki/Cyc#OpenCyc]

{2002}-OpenCyc::
"The first version of OpenCyc was released in spring 2002 and contained only 6,000 concepts and 60,000 facts. The knowledge base was released under the Apache License. Cycorp stated its intention to release OpenCyc under parallel, unrestricted licences to meet the needs of its users. The CycL and SubL interpreter (the program that allows users to browse and edit the database as well as to draw inferences) was released free of charge, but only as a binary, without source code. It was made available for Linux and Microsoft Windows. The open source Texai[15] project released the RDF-compatible content extracted from OpenCyc.[16]"
[{2021-02-08} https://en.wikipedia.org/wiki/Cyc#OpenCyc]

{1995}-CYC-project-Cycorp::
"Douglas Lenat began the project in July 1984 at MCC, where he was Principal Scientist 1984–1994, and then, since January 1995, has been under active development by the Cycorp company, where he is the CEO."
* McsEngl.{lagCnkl'1995}-Cyc-project-at-Cycorp,
[{2021-02-10} https://en.wikipedia.org/wiki/Cyc]

{1989}-lagCycl-HOL::
"By 1989,[6] CycL had expanded in expressive power to Higher Order Logic (HOL)."
[{2021-02-10} https://en.wikipedia.org/wiki/Cyc]

{1984}-CYC-project::
"Doug Lenat's CYC (from enCYClopedia) Project at MCC in Austin, a ten-year $35 million project begun in 1984, uses logic, too, but emphasizes the Aristotle-problem, employing a dozen 'ontological engineers' to enumerate tens of millions of common-sense facts that will ultimately 'add up' to logical intelligence. Lenat's goal is a system that can understand and speak ordinary language, and detect violations of common sense as readily as humans can."
[{2021-02-08} http://sandcastle.cosc.brocku.ca/~bross/3P71/misc/outsider_ai.txt-{1995-12-26}]

name::
* McsEngl.evoluting-of-lagCycl,
* McsEngl.lagCycl'evoluting,

lagCycl.SPECIFIC

description::
"Every representation is a trade-off between expressiveness (how easily you can say complicated things) and efficiency (how easily the machine can reason with what you've told it).
English is very expressive but not very efficient.
Most computer languages, such as Basic, C, and Fortran, are efficient but not very expressive.
To get both qualities, we separated the epistemological problem (what should the system know?) from the heuristic problem (how can it effectively reason with what it knows?) and developed two separate languages, respectively EL and HL.
Our knowledge enterers talk to CYC in the clean, expressive language (EL).
Their input is then converted into the heuristic language (HL), which is efficient for dealing with many sorts of frequently recurring inference problems, such as reasoning about time, causality, containment, and so forth."
[{2021-02-12} http://web.archive.org/web/20001208155800/http://www.cyc.com/halslegacy.html]

name::
* McsEngl.lagCycl.specific,

lagCycl.expressive

description::
"Every representation is a trade-off between expressiveness (how easily you can say complicated things) and efficiency (how easily the machine can reason with what you've told it).
English is very expressive but not very efficient.
Most computer languages, such as Basic, C, and Fortran, are efficient but not very expressive.
To get both qualities, we separated the epistemological problem (what should the system know?) from the heuristic problem (how can it effectively reason with what it knows?) and developed two separate languages, respectively EL and HL.
Our knowledge enterers talk to CYC in the clean, expressive language (EL).
Their input is then converted into the heuristic language (HL), which is efficient for dealing with many sorts of frequently recurring inference problems, such as reasoning about time, causality, containment, and so forth."
[{2021-02-12} http://web.archive.org/web/20001208155800/http://www.cyc.com/halslegacy.html]

name::
* McsEngl.lagCycl.EL'(expressive-language),
* McsEngl.lagCycl.expressive,

descriptionLong::
"Epistemological level refers to the way knowledge is expressed when CYC® communicates with users or external programs. This stands in contrast with heuristic level, which refers to the way knowledge is actually stored, and inference implemented, in CYC®."
[{2021-02-12} http://web.archive.org/web/20121219042246/http://www.cyc.com:80/cycdoc/ref/glossary.html#EL]

lagCycl.heuristic

description::
"Every representation is a trade-off between expressiveness (how easily you can say complicated things) and efficiency (how easily the machine can reason with what you've told it).
English is very expressive but not very efficient.
Most computer languages, such as Basic, C, and Fortran, are efficient but not very expressive.
To get both qualities, we separated the epistemological problem (what should the system know?) from the heuristic problem (how can it effectively reason with what it knows?) and developed two separate languages, respectively EL and HL.
Our knowledge enterers talk to CYC in the clean, expressive language (EL).
Their input is then converted into the heuristic language (HL), which is efficient for dealing with many sorts of frequently recurring inference problems, such as reasoning about time, causality, containment, and so forth."
[{2021-02-12} http://web.archive.org/web/20001208155800/http://www.cyc.com/halslegacy.html]

name::
* McsEngl.lagCycl.HL'(heuristic-language),
* McsEngl.lagCycl.heuristic,

descriptionLong::
"heuristic level (HL)
Another name for this might be "implementation level". Heuristic level refers to the way knowledge is actually stored, and inference implemented, in CYC®. This stands in contrast to the Epistemological Level (EL), which refers to the way knowledge is expressed when CYC® communicates with users or external programs."
[{2021-02-12} http://web.archive.org/web/20121219042246/http://www.cyc.com:80/cycdoc/ref/glossary.html#HL]

input of lagOntg

description::
· the-info we want to map with lagOntg.

name::
* McsEngl.lagOntg'input,

output|ontology of lagOntg

description::
"Ontologies are formalized vocabularies of terms, often covering a specific domain and shared by a community of users.
They specify the definitions of terms by describing their relationships with other terms in the ontology."
[{2021-01-03} https://www.w3.org/TR/owl2-overview/]

· a-model that maps the-input.
· ontology I call the-outermost system that models an-input.

"In science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between those concepts."
[{2023-08-23 retrieved} https://en.wikipedia.org/wiki/Information_science#Ontology]

name::
* McsEngl.Knlgbase.ontology!⇒kbOnto,
* McsEngl.kbOnto!=ontology--knowledge-base,
* McsEngl.lagOntg'output!⇒kbOnto,
* McsEngl.lagOntg'ontology!⇒kbOnto,
* McsEngl.ontology!⇒kbOnto,

descriptionLong::
"In information science a conceptualization is an abstract simplified view of some selected part of the world, containing the objects, concepts, and other entities that are presumed of interest for some particular purpose and the relationships between them.[2][3] An explicit specification of a conceptualization is an ontology, and it may occur that a conceptualization can be realized by several distinct ontologies.[2]"
[{2021-01-27} https://en.wikipedia.org/wiki/Conceptualization_(information_science)]
· the-above text shows the-confusion on what a-concept is and consequently an-ontology.
===
"overview of ontology:
An ontology is a formal and explicit representation of concepts, categories, properties, and their relationships within a specific domain of knowledge. Ontologies are used in various fields, including philosophy, computer science, artificial intelligence, and information science, to facilitate knowledge sharing, data integration, and reasoning. Here's an overview of ontologies:

1. **Conceptual Framework:** At its core, an ontology serves as a conceptual framework for representing knowledge. It defines the key concepts in a domain, such as objects, actions, and their properties, and the relationships between them.

2. **Formal Representation:** Ontologies are typically represented using a formal language, often based on logic or semantic web standards. This formal representation allows for precise and unambiguous definitions of concepts and their interconnections.

3. **Hierarchy:** Many ontologies include a hierarchical structure where concepts are organized in a tree-like fashion. This hierarchy allows for the categorization and classification of concepts, with more general concepts at the top and more specific ones at lower levels.

4. **Attributes and Properties:** Ontologies define attributes or properties associated with concepts. These attributes can describe characteristics, relationships, and constraints related to the concepts in the domain.

5. **Relationships:** Relationships between concepts in an ontology capture how they are connected or related to each other. Common relationship types include "is-a" (taxonomy or subclass relationships) and "part-of" relationships.

6. **Axioms and Constraints:** Ontologies often include axioms and constraints that specify rules governing the domain. These rules can express logical relationships, restrictions, and inferences.

7. **Standardization:** In many cases, ontologies adhere to standardized ontology languages and frameworks, such as the Web Ontology Language (OWL) for the Semantic Web. This helps ensure interoperability and consistency in ontology development and usage.

8. **Domain Specificity:** Ontologies can be domain-specific, addressing a particular area of knowledge, or more general, covering broader topics. For example, there are ontologies for medical terminology, geography, and even abstract concepts like time and space.

9. **Knowledge Sharing:** Ontologies facilitate knowledge sharing and data integration, as they provide a common vocabulary and structure that can be understood and used by both humans and machines. This is particularly valuable in information retrieval, data integration, and knowledge representation.

10. **Reasoning and Inference:** Ontologies support automated reasoning and inference. By applying logical rules and axioms, software systems can derive new knowledge or make deductions based on the existing ontology.

11. **Applications:** Ontologies are used in various applications, including semantic web technologies, information retrieval, natural language processing, expert systems, data integration, and more.

12. **Evolution and Maintenance:** Ontologies can evolve over time to accommodate changes in knowledge or domain understanding. Maintenance and version control are important considerations in ontology development.

Overall, ontologies play a critical role in knowledge management and knowledge representation. They provide a structured way to organize and capture knowledge, making it accessible, shareable, and interpretable by both humans and machines, and they are a fundamental component of many AI and data-related applications."
[{2023-10-20 retrieved} https://chat.openai.com/c/0af0bd57-e782-489f-b9bc-9c0c7fe8cd01]

    <foaf:Group>
     <foaf:name>ILRT staff</foaf:name>
     <foaf:member>
      <foaf:Person>
       <foaf:name>Martin Poulter</foaf:name>
       <foaf:homepage rdf:resource="http://www.ilrt.bris.ac.uk/aboutus/staff/staffprofile/?search=plmlp"/>
       <foaf:workplaceHomepage rdf:resource="http://www.ilrt.bris.ac.uk/"/>
      </foaf:Person>
     </foaf:member>
    </foaf:Group>
    

    <!-- here we see a FOAF group described.
         each foaf group may be associated with an OWL definition
         specifying the class of agents that constitute the group's membership -->
    <foaf:Group>
     <foaf:name>ILRT staff</foaf:name>
     <foaf:membershipClass>
        <owl:Class rdf:about="http://ilrt.example.com/groups#ILRTStaffPerson">
         <rdfs:subClassOf rdf:resource="http://xmlns.com/foaf/0.1/Person"/>
         <rdfs:subClassOf>
           <owl:Restriction>
             <owl:onProperty rdf:resource="http://xmlns.com/foaf/0.1/workplaceHomepage"/>
             <owl:hasValue rdf:resource="http://www.ilrt.bris.ac.uk/"/>
           </owl:Restriction>
         </rdfs:subClassOf>
       </owl:Class>
     </foaf:membershipClass>
    </foaf:Group>
    

    <foaf:Group>
     <foaf:member rdf:nodeID="martin"/>
     <!-- more about the group here -->
    </foaf:Group>
    <foaf:Person rdf:nodeID="martin">
      <!-- more about martin here -->
    </foaf:Person>
    

    <foaf:Person rdf:nodeID="p1">
     <foaf:name>Dan Brickley</foaf:name>
     <foaf:homepage rdf:resource="http://danbri.org/"/>
     <!-- etc... -->
    </foaf:Person>

    <foaf:PersonalProfileDocument rdf:about="">
       <foaf:maker rdf:nodeID="p1"/>
       <foaf:primaryTopic rdf:nodeID="p1"/>
    </foaf:PersonalProfileDocument>
    

    <foaf:Person>
      <foaf:name>Dan Brickley</foaf:name>
      <foaf:account>
        <foaf:OnlineAccount>
          <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/OnlineChatAccount"/>
          <foaf:accountServiceHomepage
                   rdf:resource="http://www.freenode.net/"/>
          <foaf:accountName>danbri</foaf:accountName>
        </foaf:OnlineAccount>
      </foaf:account>
    </foaf:Person>