human of lagCnkl

description::
· the-human that uses the-language to map his mind-view.

name::
* McsEngl.lagCnkl'human,

manager (link) of lagCnkl

domain of Cnptlinput

description::
· the-set of input-views we want to map.

name::
* McsEngl.Cnptlinput'domain,
* McsEngl.lagCnkl'domain,

schema of Cnptlinput

description::
· input-schema is the-structure of the-input-view expressed in a-lagCnkl.

name::
* McsEngl.Cnptlinput'schema,
* McsEngl.lagCnkl'input-schema,
* McsEngl.input-schema-of-lagCnkl,

mind-concept of Cnptlinput

description::
· a-mind-concept that is-mapped.

name::
* McsEngl.Cnptlicpt,
* McsEngl.Cnptlinput'mind-concept!⇒Cnptlicpt,
* McsEngl.lagCnkl'input-concept!⇒Cnptlicpt,

mind-view of Cnptlinput

description::
· a-system of mind-concepts that is-mapped.

name::
* McsEngl.Cnptlinput'view!⇒Cnptliview,
* McsEngl.Cnptliview,
* McsEngl.lagCnkl'input-view!⇒Cnptliview,

mind-worldview of Cnptlinput

description::
· all the-mind-views of one author that is-mapped.

name::
* McsEngl.Cnptlinput'worldview!⇒Cnptliworld,
* McsEngl.Cnptliworld,
* McsEngl.lagCnkl'input-worldview!⇒Cnptliworld,

mind-base of Cnptlinput

description::
· the-set of input-worldviews that is-mapped.

name::
* McsEngl.Cnptlibase,
* McsEngl.Cnptlinput'base!⇒Cnptlibase,
* McsEngl.lagCnkl'input-base!⇒Cnptlibase,

output-schema of lagCnkl

description::
· output-schema is the-structure of the-output-view written in a-lagCnkl.

name::
* McsEngl.Cnptloutput'schema,
* McsEngl.lagCnkl'schema,
* McsEngl.output-schema-of-lagCnkl,

output-concept of lagCnkl

description::
· a-model of an-input-concept using the-lagCnkl.

name::
* McsEngl.Cnptlcpt,
* McsEngl.lagCnkl'output-concept!⇒Cnptlcpt,

output-view of lagCnkl

description::
· a-system of output-concepts.

name::
* McsEngl.Cnptlview,
* McsEngl.lagCnkl'output-view!⇒Cnptlview,

output-worldview of lagCnkl

description::
· all the-output-views of one author.

name::
* McsEngl.Cnptlworld,
* McsEngl.lagCnkl'output-worldview!⇒Cnptlworld,

output-base of lagCnkl

description::
· the-set of output-worldviews.

name::
* McsEngl.Cnptlbase,
* McsEngl.knowledge-base-of-Cnklmngr!⇒Cnptlbase,
* McsEngl.lagCnkl'output-base!⇒Cnptlbase,

knowledge-base of Cnklmngr

description::
· the-output of implementing a-concept--knowledge-language.

name::
* McsEngl.Cnklmngr'knowledge-base,
* McsEngl.Knlgbase.concept-knowledge-language--manager,

hard-sys of Cnklmngr

description::
· computer, network used.

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

soft-sys of Cnklmngr

description::
· any application used.

name::
* McsEngl.Cnptlapp,
* McsEngl.lagCnkl'01_02_app!⇒Cnptlapp,
* McsEngl.Cnklmngr.app!⇒Cnptlapp,
* McsEngl.Cnptltool,
* McsEngl.lagCnkl'01_03_tool!⇒Cnptltool,
* McsEngl.lagCnkl'tool!⇒Cnptltool,
* McsEngl.Cnklmngr.tool,

specific-tree-of-::
* machine-learning-system,
* machine-reasoning-system,
* machine-translating-system,
* natural-language-processing-system,
* speech-recognition-system,
* speech-to-text-system,
* text-to-speech-system,

info-resource of Cnklmngr

description::
* https://synagonism.net/HSBC.2010-08-23/hSbc/krs.html,

name::
* McsEngl.Cnklmngr'Infrsc,

structure of Cnklmngr

description::
* tech-system,
* application,
* base,

name::
* McsEngl.Cnklmngr'structure,

DOING of Cnklmngr

description::
* building-base,
* retrieving-base,
===
* development,
* encoding,
* decoding,
* quering,
* reasoning,
** integrating,

name::
* McsEngl.Cnklmngr'doing,

Cnklmngr.SPECIFIC

description::
* Mcsmgr,
** Hmcsmgr,
=== generic:
* artificial-intelligence-system,
* collaborative-system,
* conceptual-graphs-system,
* decision-support-system,
* Description-logic-system,
* Design-system,
* Diagnosis-system,
* Expert-system,
* Frame-based-system,
* Java-system,
* Lisp-system,
* logic-system,
* Mcsmgr,
* ontology-system,
* planning/scheduling-system,
* process-control-system,
* rule-based-system,
* Smalltalk-system,
* web-system,
=== individual:
* CODE4,
* ConceptBase,
* Cyc,
* McsmgrAAj,
* McsmgrHitp,
* McsmgrFv,
* Protege,
* WolframAlpha,
* WordNet,

name::
* McsEngl.Cnklmngr.specific,

expressiveness-vs-efficiency of lagCnkl

description::
"Expressiveness in a representation language is the ability to express facts, rules, and relationships quickly, easily, and in a principled and factored way.
Efficiency in a representation language is the ability to conclude new facts from existing facts as quickly and easily as possible, with as much coverage as possible...
Most natural languages, for example English, would have a high expressiveness but a low efficiency if used as a knowledge representation language".
[http://www.cyc.com/doc/handbook/oe/06-el-hl-and-the-canonicalizer.html]

name::
* McsEngl.lagCnkl'expressiveness-vs-efficiency,

specification of Cnptlrsc

description::
· official formal documentation of the-language.

name::
* McsEngl.Cnptlrsc'specification,
* McsEngl.lagCnkl'specification,

data-exchange-language of Cnptlrsc

description::
"A data interchange (or exchange) language/format is a language that is domain-independent and can be used for data from any kind of discipline.[9] They have "evolved from being markup and display-oriented to further support the encoding of metadata that describes the structural attributes of the information."[10]
Practice has shown that certain types of formal languages are better suited for this task than others, since their specification is driven by a formal process instead of particular software implementation needs. For example, XML is a markup language that was designed to enable the creation of dialects (the definition of domain-specific sublanguages).[11] However, it does not contain domain-specific dictionaries or fact types. Beneficial to a reliable data exchange is the availability of standard dictionaries-taxonomies and tools libraries such as parsers, schema validators, and transformation tools.[citation needed]
Popular languages used for data exchange
* RDF,
* XML,
* Atom,
* JSON,
* YAML,
* REBOL,
* Gellish"
[{2021-01-11} https://en.wikipedia.org/wiki/Data_exchange]

name::
* McsEngl.Cnptlrsc'data-exchange-language,
* McsEngl.data-exchange-language,
* McsEngl.lagCnkl'data-exchange-language,

modeling-language of Cnptlrsc

description::
"A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure."
[{2021-01-11} https://en.wikipedia.org/wiki/Modeling_language]

name::
* McsEngl.Cnptlrsc'modeling-language,
* McsEngl.lagCnkl'modeling-language,
* McsEngl.modeling-language,

formal-concept-analysis of Cnptlrsc

description::
"Formal Concept Analysis (FCA) is a method for data analysis, knowledge representation and information management that is widely unknown among information scientists in the USA even though this technology has a significant potential for applications. FCA was invented by Rudolf Wille in the early 80s (Wille, 1982). For the first 10 years, FCA was developed mainly by a small group of researchers and Wille’s students in Germany. Because of the mathematical nature of most of the publications of that time, knowledge of FCA remained restricted to a group of “insiders”. Through funded research projects, FCA was implemented in several larger-scale applications, most notably an implementation of a knowledge exploration system for civil engineering in cooperation with the Ministry for Civil Engineering of North-Rhine Westfalia (cf. Eschenfelder et al. (2000)). But these applications were not publicized widely beyond Germany."
[{2021-02-06} https://www.upriss.org.uk/papers/arist.pdf]

name::
* McsEngl.Cnptlrsc'formal-concept-analysis,
* McsEngl.FCA'(formal-concept-analysis),
* McsEngl.formal-concept-analysis,

addressWpg::
* https://upriss.github.io/fca/fcaintro.html,
* https://upriss.github.io/fca/fca.html,
* https://en.wikipedia.org/wiki/Formal_concept_analysis,

lagCvkl.UML

description::
"The Unified Modeling Language™ (UML®) is a standard visual modeling language intended to be used for
* modeling business and similar processes,
* analysis, design, and implementation of software-based systems
UML is a common language for business analysts, software architects and developers used to describe, specify, design, and document existing or new business processes, structure and behavior of artifacts of software systems.
UML can be applied to diverse application domains (e.g., banking, finance, internet, aerospace, healthcare, etc.) It can be used with all major object and component software development methods and for various implementation platforms (e.g., J2EE, .NET)."
[{2021-02-07} https://www.uml-diagrams.org/]

name::
* McsEngl.UML!=unified-modeling-language,
* McsEngl.lagCnkl.035-UML!⇒lagUmlg,
* McsEngl.lagCnkl.UML!⇒lagUmlg,
* McsEngl.lagCvkl.UML!⇒lagUmlg,

lagCvkl.concept-mapping

description::
"A concept map or conceptual diagram is a diagram that depicts suggested relationships between concepts.[1] Concept maps may be used by instructional designers, engineers, technical writers, and others to organize and structure knowledge.
A concept map typically represents ideas and information as boxes or circles, which it connects with labeled arrows, often in a downward-branching hierarchical structure. The relationship between concepts can be articulated in linking phrases such as "causes", "requires", "such as" or "contributes to".[2]
The technique for visualizing these relationships among different concepts is called concept mapping. Concept maps have been used to define the ontology of computer systems, for example with the object-role modeling or Unified Modeling Language formalism."
[{2021-03-19} https://en.wikipedia.org/wiki/Concept_map]

name::
* McsEngl.concept-mapping,
* McsEngl.lagCnkl.038-concept-mapping,
* McsEngl.lagCnkl.concept-mapping,

lagCvkl.VUE

description::
What is VUE?
At its core, the Visual Understanding Environment (VUE) is a concept and content mapping application, developed to support teaching, learning and research and for anyone who needs to organize, contextualize, and access digital information. Using a simple set of tools and a basic visual grammar consisting of nodes and links, faculty and students can map relationships between concepts, ideas and digital content.

Concept mapping is not new to the educational field. In fact, the benefits of concept mapping as a learning tool have been documented by over 40 years of cognitive science research. VUE provides a concept mapping interface, which can be used as such, or as an interface to organize digital content in non-linear ways.

Numerous tools currently exist for locating digital information, but few applications are available for making sense of the information available to us. As the availability of digital information continues to increase, VUE sets itself apart as a flexible tool to help faculty and students integrate, organize and contextualize electronic content in their work. Digital content can be accessed via the Web, or using the VUE’s “Resources” panel to tap into digital repositories, FTP servers and local file systems.

Sharing and presenting information are important aspects of academic work. VUE’s pathways feature allows presenters to create annotated trails through their maps, which become expert guided walk-throughs of the information. The pathways feature also provides a “slide view” of the information on the map. The power of VUE’s slide mode is the ability for presenters to focus on content (slide view) while preserving the information’s context (map view), by way of a single toggle between the two views.

VUE also provides supports for in-depth analysis of maps, with the ability to merge maps and export connectivity matrices to import in statistical packages. VUE also provide tools to apply semantic meaning to the maps, by way of ontologies and metadata schemas.

VUE can be used by anyone interested in visually structuring digital content, whether in support of teaching difficult to understand concepts or more generally, a tool for organizing personal digital resources.
[{2023-11-09 retrieved} https://vue.tufts.edu/about]

name::
* McsEngl.VUE!=Visual-Understanding-Environment,
* McsEngl.lagCvkl.VUE,
* McsEngl.lagCnkl.041-VUE,
* McsEngl.lagCnkl.VUE,

lagCnklMrkp.SPECIFIC

description::
* Xml,
* lagMcsh,

name::
* McsEngl.lagCnklMrkp.specific,

input of lagFram

description::
·

name::
* McsEngl.lagFram'input,

output of lagFram

description::
·

name::
* McsEngl.lagFram'output,

evaluation of lagFram

description::
· a-frame-language is a-concept-language because 'frames' are actually concepts.

name::
* McsEngl.lagFram'evaluation,

manager of lagFram

description::
· the-knowledge-manager of a lagFram.

name::
* McsEngl.Frmlmngr!=frame-language--manager,
* McsEngl.frame-based-system!⇒Frmlmngr,
* McsEngl.lagFram'manager!⇒Frmlmngr,

specific-tree-of-Frmlmngr::
* Cyc,
* FrameNet,
* KL-ONE,
* OntoFrame,

info-resource of lagFram

description::
·

name::
* McsEngl.lagFram'Infrsc,

evoluting of lagFram

description::
·

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

lagFram.SPECIFIC

description::
* CycL,
* KEE,
* KL-ONE,
* KRL,
* Loom,

name::
* McsEngl.lagFram.specific,

output of lagSnet

description::
"A knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – with free-form semantics.[1]
Since the development of the Semantic Web, knowledge graphs are often associated with linked open data projects, focusing on the connections between concepts and entities.[2][3] They are also prominently associated with and used by search engines such as Google, Bing, and Yahoo; knowledge-engines and question-answering services such as WolframAlpha, Apple's Siri, and Amazon Alexa; and social networks such as LinkedIn and Facebook."
[{2021-01-31} https://en.wikipedia.org/wiki/Knowledge_graph]

name::
* McsEngl.Snetoutput,
* McsEngl.knowledge-graph!⇒Snetoutput,
* McsEngl.lagSnet'output!⇒Snetoutput,
* knowledge-base,

addressWpg::
* https://www.stardog.com/blog/what-is-a-knowledge-graph/, and its relation with database-graph,

evoluting of lagSnet

description::
"evolution of semantic-networks:
Semantic networks are a type of knowledge representation used in artificial intelligence and cognitive science to model the organization of concepts and their relationships. They have evolved over time, and their development can be divided into several stages:

1. **Early Cognitive Models (1950s - 1960s):** The roots of semantic networks can be traced back to early cognitive psychology and linguistics. Researchers like Ross Quillian and Allan M. Collins developed some of the earliest models of semantic networks during this period. They proposed simple networks of concepts linked by "is-a" or "kind-of" relationships.

2. **Conceptual Dependency Theory (1970s):** In the 1970s, researchers like Schank and Riesbeck developed the Conceptual Dependency Theory, which extended semantic networks by incorporating a broader range of relationships, such as "agent," "patient," and "instrument." These relationships allowed for more complex knowledge representation.

3. **Frame-Based Systems (1970s - 1980s):** Frames, introduced by Marvin Minsky, are structures that contain information about a specific concept, including its attributes, relationships, and behaviors. Frames serve as the basis for knowledge representation in semantic networks, enabling the representation of richer information about concepts.

4. **Scripts (1970s - 1980s):** Roger Schank's concept of scripts extended semantic networks by introducing the idea of representing stereotypical sequences of events or actions. Scripts are essentially templates for common real-world scenarios and actions.

5. **Connectionist Models (1980s - 1990s):** Connectionist models, such as neural networks, began to influence the development of semantic networks. These models used distributed representations and connection weights to capture the meaning of words and concepts. These representations were less symbolic and more data-driven.

6. **Ontologies and Knowledge Graphs (1990s - Present):** With the advent of the World Wide Web, there was a growing need for more structured and standardized knowledge representation. This led to the development of ontologies and knowledge graphs. Ontologies like the Semantic Web's RDF (Resource Description Framework) and OWL (Web Ontology Language) allowed for the creation of structured, machine-readable representations of knowledge. Knowledge graphs, such as Google's Knowledge Graph, organized vast amounts of structured data, allowing search engines to provide more accurate and context-aware information.

7. **Word Embeddings and Neural Networks (2010s - Present):** In recent years, word embeddings and deep learning methods have become popular for natural language understanding and knowledge representation. Techniques like Word2Vec, GloVe, and BERT have enabled the development of more context-aware and semantically rich representations of words and concepts.

8. **Hybrid Approaches (Present):** Today, semantic networks often combine various methods, including symbolic and connectionist approaches. Hybrid models that use both symbolic representations and distributed representations have gained popularity for tasks like natural language understanding and reasoning.

The evolution of semantic networks reflects the ongoing efforts to create more sophisticated and accurate models for representing and understanding human knowledge and language. As AI and cognitive science continue to advance, semantic networks will likely continue to evolve and adapt to new challenges and opportunities."
[{2023-10-24 retrieved} https://chat.openai.com/c/5596bf85-a830-4ee1-9a2c-5c5a1eb3c481]

"evolution of semantic-networks:
Semantic networks are a type of knowledge representation that uses nodes and edges to represent concepts and relationships between them. They have been used in a variety of artificial intelligence applications, including natural language processing, machine learning, and expert systems.

The evolution of semantic networks can be traced back to the early days of artificial intelligence, with early examples such as the Cyc project and the Semantic Net project. However, it was not until the late 1980s and early 1990s that semantic networks began to be widely used in practical applications.

One of the main driving forces behind the evolution of semantic networks has been the development of new algorithms for knowledge representation and reasoning. For example, the development of description logics in the 1990s provided a more powerful and expressive way to represent knowledge in semantic networks.

Another important factor in the evolution of semantic networks has been the development of new technologies for knowledge acquisition and sharing. For example, the Semantic Web provides a framework for sharing and interlinking knowledge across the internet.

Semantic networks are now widely used in a variety of applications, including:
* **Natural language processing:** Semantic networks are used to represent the meaning of words and sentences, and to support tasks such as machine translation, text summarization, and question answering.
* **Machine learning:** Semantic networks can be used to represent the features of data and to support tasks such as classification, regression, and clustering.
* **Expert systems:** Semantic networks can be used to represent the knowledge of experts in a particular domain, and to support tasks such as diagnosis, decision making, and planning.

Here are some specific examples of how semantic networks are being used today:
* **Google Search:** Google Search uses semantic networks to understand the meaning of search queries and to provide relevant results.
* **Wikipedia:** Wikipedia uses semantic networks to represent the relationships between different articles.
* **Facebook:** Facebook uses semantic networks to recommend friends to users and to target advertising.
* **Amazon:** Amazon uses semantic networks to recommend products to customers.

Semantic networks are a powerful tool for representing and reasoning about knowledge. They are used in a wide variety of applications, and their importance is likely to continue to grow in the future.

**Recent trends in the evolution of semantic networks**
In recent years, there has been a growing interest in the use of deep learning to improve the performance of semantic networks. For example, deep learning can be used to learn more expressive representations of concepts and relationships, and to develop more efficient algorithms for reasoning.

Another recent trend is the development of new methods for integrating semantic networks with other knowledge representation and reasoning technologies, such as ontologies and rule engines. This integration can lead to more powerful and flexible knowledge-based systems.

Overall, the evolution of semantic networks is a dynamic and exciting area of research. Semantic networks are becoming increasingly powerful and versatile, and they are being used in a wider range of applications."
[{2023-10-24 retrieved} https://bard.google.com/chat/fe395aa6c33362c7]

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

lagSnet.SPECIFIC

description::
·

name::
* McsEngl.lagSnet.specific,

lagSnet.knowledge-graph

description::
"evolution of knowledge-graphs:
Knowledge graphs (KGs) have evolved significantly since their early days. The first KGs were hand-crafted and relatively small, but they quickly grew in size and complexity as researchers developed new techniques for knowledge extraction and reasoning. Today, KGs are used in a wide range of applications, including search, question answering, and machine learning.

One of the key trends in the evolution of KGs has been the shift from entity-based to text-rich graphs. Entity-based KGs represent knowledge as a collection of entities and their relationships. Text-rich KGs, on the other hand, allow for the inclusion of free text descriptions of entities and relationships. This makes text-rich KGs more expressive and easier to maintain and update.

Another important trend is the emergence of dual-neural graphs. Dual-neural graphs combine entity-based and text-rich representations of knowledge. This allows them to take advantage of the strengths of both approaches, while overcoming some of their limitations. For example, dual-neural graphs can be more robust to noise in the data and can better capture the context of knowledge.

Here is a timeline of some of the key milestones in the evolution of knowledge graphs:
* **1970s:** The first KGs are developed, such as Cyc and SUMO. These KGs are hand-crafted and relatively small.
* **1980s and 1990s:** Researchers develop new techniques for knowledge extraction, such as rule-based systems and machine learning algorithms. This leads to the development of larger and more complex KGs.
* **2000s:** The Web of Data is launched, providing a framework for publishing and sharing KGs on the web. This leads to the development of large open KGs, such as DBpedia and Wikidata.
* **2010s:** Dual-neural graphs are developed. These graphs combine entity-based and text-rich representations of knowledge, which makes them more expressive and easier to maintain and update.

Today, KGs are used in a wide range of applications, including:
* **Search:** KGs can be used to improve the accuracy and relevancy of search results. For example, a search engine can use a KG to understand the context of a query and to provide more relevant results.
* **Question answering:** KGs can be used to answer complex questions in a comprehensive and informative way. For example, a question answering system can use a KG to answer questions about the relationships between different entities.
* **Machine learning:** KGs can be used to train and improve the performance of machine learning models. For example, a machine learning model can be trained on a KG to learn how to classify or predict entities.

The evolution of knowledge graphs is still ongoing. Researchers are developing new techniques for knowledge extraction, reasoning, and machine learning. This is leading to the development of more powerful and expressive KGs that can be used in a wider range of applications."
[{2023-10-26 retrieved} https://bard.google.com/chat/4fabb07577c0cab0]
===
"evolution of knowledge-graphs:
Knowledge graphs have evolved significantly over the years, driven by advancements in technology and the growing need for organizing and making sense of vast amounts of data. Here is a brief overview of the evolution of knowledge graphs:

1. Early Databases and Semantic Networks:
- The concept of organizing information in a network-like structure can be traced back to early databases and semantic networks in the 1960s and 1970s.
- Systems like the Generalized Markup Language (GML) and early versions of the Semantic Web laid the foundation for structuring data in a graph format.

2. RDF and the Semantic Web:
- The development of the Resource Description Framework (RDF) in the late 1990s marked a significant step in the evolution of knowledge graphs.
- RDF provided a standardized way to represent data in a subject-predicate-object format, which forms the basis of many modern knowledge graphs.
- The Semantic Web project aimed to interconnect these RDF data sources, creating a web of linked data.

3. Ontologies and Taxonomies:
- Knowledge graphs often incorporate ontologies and taxonomies to provide a structured framework for data.
- Ontologies, such as OWL (Web Ontology Language), enable the formal definition of concepts, relationships, and properties within a domain.
- Taxonomies help organize data hierarchically, facilitating categorization and classification.

4. Graph Databases:
- The emergence of graph databases in the early 2000s, such as Neo4j and AllegroGraph, provided a dedicated platform for storing and querying graph-structured data efficiently.
- These databases enable the representation and retrieval of data as graphs, making them well-suited for knowledge graph applications.

5. Linked Data:
- Tim Berners-Lee's Linked Data principles, introduced in the late 2000s, encouraged publishing structured data on the web using RDF and URIs.
- This approach led to the creation of an interconnected web of data, promoting the growth of knowledge graphs.

6. Knowledge Representation and Inference:
- Knowledge graphs have evolved to include inference and reasoning capabilities, allowing for the deduction of new information from existing data.
- Technologies like RDFS (Resource Description Framework Schema) and OWL support reasoning over knowledge graphs.

7. Machine Learning and NLP Integration:
- Recent advancements in machine learning and natural language processing (NLP) have allowed for the automated extraction of structured data from unstructured text, contributing to knowledge graph enrichment.
- Machine learning techniques have been applied to knowledge graph completion and entity resolution.

8. Industry Applications:
- Knowledge graphs have found applications in various industries, including healthcare (patient records and drug interactions), e-commerce (product recommendations), finance (fraud detection), and more.
- They are used for search engines, recommendation systems, and data integration in many domains.

9. Evolving Standards:
- Standards and tools for working with knowledge graphs continue to evolve. Initiatives like the W3C RDF Working Group and various open-source projects contribute to the development of best practices and technologies.

The evolution of knowledge graphs is ongoing, and they continue to play a crucial role in organizing and making data more accessible and meaningful in a variety of fields. Their potential for helping computers understand and reason about information makes them a key component of the future of artificial intelligence and data management."
[{2023-10-26 retrieved} https://chat.openai.com/c/370c76cc-511e-4b23-82ae-eb7dd6e2f17b]

name::
* McsEngl.KG!=knowledge-graph!⇒lagKngr,
* McsEngl.knowledge-graph!⇒lagKngr,
* McsEngl.lagCnkl.042-knowledge-graph!⇒lagKngr,
* McsEngl.lagCnkl.knowledge-graph!⇒lagKngr,
* McsEngl.lagKngr!=knowledge-graph--language,
* McsEngl.lagSnet.knowledge-graph!⇒lagKngr,

lagSnet.concept-map

description::
"overview of concept-maps:
A concept map is a visual tool used to represent and organize knowledge or ideas in a structured and interconnected manner. Concept maps are graphical diagrams that consist of nodes (or concepts) and links (or relationships) between those concepts. They are often used in education, problem-solving, brainstorming, and knowledge management to help individuals or groups better understand, communicate, and navigate complex topics. Here's an overview of concept maps:

**Components of Concept Maps:**
1. **Concepts (Nodes):** Concepts are represented as nodes in a concept map. They can be words, phrases, or symbols that encapsulate specific ideas or topics. Each concept typically contains a label or description.

2. **Relationships (Links):** Relationships between concepts are represented as lines or arrows connecting the nodes. These links indicate how concepts are related or connected to one another. Relationships may include various types such as "is a part of," "causes," "leads to," "is a type of," etc.

3. **Cross-links:** In more complex concept maps, cross-links can connect concepts from different areas of the map, showing how seemingly unrelated concepts can be linked in a meaningful way.

4. **Examples and Descriptions:** Concept maps often include examples or additional information to clarify the concepts and relationships.

**Characteristics of Concept Maps:**
1. **Hierarchy:** Concept maps can have hierarchical structures with main concepts at the top and sub-concepts branching below. This hierarchy helps in organizing information from general to specific.

2. **Nonlinear Structure:** Unlike outlines or linear text, concept maps are non-linear, allowing for complex relationships and multiple connections among concepts.

3. **Visual Representation:** The visual nature of concept maps makes them effective for visual learners and for conveying complex ideas more intuitively.

4. **Dynamic:** Concept maps can evolve as knowledge grows or as more connections and relationships are discovered. They are not static and can be updated or revised.

**Uses of Concept Maps:**
1. **Education:** Concept maps are widely used in education as a tool for learning and teaching. Students create concept maps to summarize and organize their understanding of a subject, while educators use concept maps to illustrate complex topics and facilitate discussions.

2. **Problem Solving:** Concept maps can aid in problem-solving by helping individuals break down complex problems into simpler components and explore possible solutions.

3. **Knowledge Management:** In organizations, concept maps can be used to capture and represent organizational knowledge, making it easier to navigate and transfer knowledge among employees.

4. **Brainstorming:** Teams and individuals use concept maps to brainstorm ideas, concepts, and solutions. They help in generating creative ideas and understanding the relationships between them.

5. **Research and Planning:** Researchers often use concept maps to visualize research topics, hypotheses, and data relationships. They can also be used in project planning to clarify project scope and objectives.

6. **Communication:** Concept maps can serve as a communication tool for sharing complex information with others in a clear and concise manner.

Concept maps are versatile and can be adapted to a wide range of applications and contexts. They are a valuable tool for promoting critical thinking, knowledge organization, and knowledge sharing."
[{2023-10-24 retrieved} https://chat.openai.com/c/b772e8ef-c6be-489f-8ad4-69895bc95406]

name::
* McsEngl.lagCnkl.032-concept-map!⇒lagCtmp,
* McsEngl.lagCnkl.concept-map!⇒lagCtmp,
* McsEngl.lagCtmp,
* McsEngl.lagCtmp!=concept-map,
* McsEngl.lagSnet.concept-map!⇒lagCtmp,
* McsEngl.concept-map!⇒lagCtmp,

descriptionLong::
"A concept map or conceptual diagram is a diagram that depicts suggested relationships between concepts.[1] Concept maps may be used by instructional designers, engineers, technical writers, and others to organize and structure knowledge.
A concept map typically represents ideas and information as boxes or circles, which it connects with labeled arrows, often in a downward-branching hierarchical structure. The relationship between concepts can be articulated in linking phrases such as "causes", "requires", "such as" or "contributes to".[2]
The technique for visualizing these relationships among different concepts is called concept mapping. Concept maps have been used to define the ontology of computer systems, for example with the object-role modeling or Unified Modeling Language formalism."
[{2021-02-07} https://en.wikipedia.org/wiki/Concept_map]

machine of lagXmlg

description::
· any computer.

name::
* McsEngl.lagXmlg'machine,

input of lagXmlg

description::
· lagXmlg maps simple conceptual-views eg a-person[a] with some attributes of it[a].

name::
* McsEngl.lagXmlg'input,

output of lagXmlg

description::
· the-output is a-markup-document with Xmlelements denoting concepts or attributes of concepts.

name::
* McsEngl.Xmldoc,
* McsEngl.lagXmlg'instance-document!⇒Xmldoc,
* McsEngl.lagXmlg'output!⇒Xmldoc,

"Example
    The Purchase Order Schema, po.xsd
    <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema">

      <xsd:annotation>
        <xsd:documentation xml:lang="en">
         Purchase order schema for Example.com.
         Copyright 2000 Example.com. All rights reserved.
        </xsd:documentation>
      </xsd:annotation>

      <xsd:element name="purchaseOrder" type="PurchaseOrderType"/>

      <xsd:element name="comment" type="xsd:string"/>

      <xsd:complexType name="PurchaseOrderType">
        <xsd:sequence>
          <xsd:element name="shipTo" type="USAddress"/>
          <xsd:element name="billTo" type="USAddress"/>
          <xsd:element ref="comment" minOccurs="0"/>
          <xsd:element name="items"  type="Items"/>
        </xsd:sequence>
        <xsd:attribute name="orderDate" type="xsd:date"/>
      </xsd:complexType>

      <xsd:complexType name="USAddress">
        <xsd:sequence>
          <xsd:element name="name"   type="xsd:string"/>
          <xsd:element name="street" type="xsd:string"/>
          <xsd:element name="city"   type="xsd:string"/>
          <xsd:element name="state"  type="xsd:string"/>
          <xsd:element name="zip"    type="xsd:decimal"/>
        </xsd:sequence>
        <xsd:attribute name="country" type="xsd:NMTOKEN"
                       fixed="US"/>
      </xsd:complexType>

      <xsd:complexType name="Items">
        <xsd:sequence>
          <xsd:element name="item" minOccurs="0" maxOccurs="unbounded">
            <xsd:complexType>
              <xsd:sequence>
                <xsd:element name="productName" type="xsd:string"/>
                <xsd:element name="quantity">
                  <xsd:simpleType>
                    <xsd:restriction base="xsd:positiveInteger">
                      <xsd:maxExclusive value="100"/>
                    </xsd:restriction>
                  </xsd:simpleType>
                </xsd:element>
                <xsd:element name="USPrice"  type="xsd:decimal"/>
                <xsd:element ref="comment"   minOccurs="0"/>
                <xsd:element name="shipDate" type="xsd:date" minOccurs="0"/>
              </xsd:sequence>
              <xsd:attribute name="partNum" type="SKU" use="required"/>
            </xsd:complexType>
          </xsd:element>
        </xsd:sequence>
      </xsd:complexType>

      <!-- Stock Keeping Unit, a code for identifying products -->
      <xsd:simpleType name="SKU">
        <xsd:restriction base="xsd:string">
          <xsd:pattern value="\d{3}-[A-Z]{2}"/>
        </xsd:restriction>
      </xsd:simpleType>

    </xsd:schema>

    of
    The Purchase Order, po.xml
    <?xml version="1.0"?>
    <purchaseOrder orderDate="1999-10-20">
       <shipTo country="US">
          <name>Alice Smith</name>
          <street>123 Maple Street</street>
          <city>Mill Valley</city>
          <state>CA</state>
          <zip>90952</zip>
       </shipTo>
       <billTo country="US">
          <name>Robert Smith</name>
          <street>8 Oak Avenue</street>
          <city>Old Town</city>
          <state>PA</state>
          <zip>95819</zip>
       </billTo>
       <comment>Hurry, my lawn is going wild</comment>
       <items>
          <item partNum="872-AA">
             <productName>Lawnmower</productName>
             <quantity>1</quantity>
             <USPrice>148.95</USPrice>
             <comment>Confirm this is electric</comment>
          </item>
          <item partNum="926-AA">
             <productName>Baby Monitor</productName>
             <quantity>1</quantity>
             <USPrice>39.98</USPrice>
             <shipDate>1999-05-21</shipDate>
          </item>
       </items>
    </purchaseOrder>"

evaluation of lagXmlg

description::
·

name::
* McsEngl.lagXmlg'evaluation,

tool of lagXmlg

description::
·

name::
* McsEngl.lagXmlg'tool,

info-resource of lagXmlg

description::
* https://relaxng.org/, a-schema-language for Xmlg,

name::
* McsEngl.lagXmlg'Infrsc,

DOING of lagXmlg

description::
* parsing,
* validating,

name::
* McsEngl.lagXmlg'doing,

evoluting of lagXmlg

{2012-04}-XML-Schema-1.1::
"XSD 1.1 became a W3C Recommendation in April 2012, which means it is an approved W3C specification."
[{2021-01-14} https://en.wikipedia.org/wiki/XML_Schema_(W3C)]

{2001-05}-XML-Schema::
"XML Schema, published as a W3C recommendation in May 2001,[2] is one of several XML schema languages. It was the first separate schema language for XML to achieve Recommendation status by the W3C. Because of confusion between XML Schema as a specific W3C specification, and the use of the same term to describe schema languages in general, some parts of the user community referred to this language as WXS, an initialism for W3C XML Schema, while others referred to it as XSD, an initialism for XML Schema Definition.[3][4] In Version 1.1 the W3C has chosen to adopt XSD as the preferred name, and that is the name used in this article."
* McsEngl.{lagCnkl'2001-05}-XML-Schema,
[{2021-01-14} https://en.wikipedia.org/wiki/XML_Schema_(W3C)]

{1996}-XML-creation::
"XML was developed by an XML Working Group (originally known as the SGML Editorial Review Board) formed under the auspices of the World Wide Web Consortium (W3C) in 1996. It was chaired by Jon Bosak of Sun Microsystems with the active participation of an XML Special Interest Group (previously known as the SGML Working Group) also organized by the W3C. The membership of the XML Working Group is given in an appendix. Dan Connolly served as the Working Group's contact with the W3C."
* McsEngl.{lagCnkl'1996}-XML-creation,
[{2021-01-12} https://www.w3.org/TR/2006/REC-xml11-20060816/#sec-origin-goals]

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

lagXmlg.SPECIFIC

description::
·

name::
* McsEngl.lagXmlg.specific,

addressWpg::
* https://en.wikipedia.org/wiki/List_of_XML_markup_languages,
* https://en.wikipedia.org/wiki/List_of_types_of_XML_schemas,