Knowledge Modeling for AI Collaboration

Part 3 organized a SimpleModeling Model into three constituents: Object, Knowledge, and Literate Models. Part 4 explained how Views select the required elements from the three according to Purpose and Concern. Part 5 examined the Object Model, which carries formal structure.

Part 6 examines the Knowledge Model. It is not merely documentation or a source list for the Object Model, but a constituent that expresses the knowledge aspect of the same subject. The next article on Literate Modeling will examine the Literate Model, which retains context, intent, requirements, decisions, and scenarios primarily in natural language people can understand.

Previous article: 📄 Object Modeling as a Structural Foundation

Object, Knowledge, and Literate Models are constituents of a SimpleModeling Model that express different aspects of the same subject in forms suited to their users and purposes. A Knowledge Model is not explanatory material or a source list for the Object Model. It expresses the knowledge aspect by connecting concepts, classifications, semantic relationships, rules, evidence, and sources so generative AI can explore them and explain them to people.

The three are not a classification that bundles unrelated documents afterward. Through terms defined in the Glossary, formal structures, knowledge structures, and natural-language descriptions are mapped to one another. Domain, Use Case, and Application Models are composed by selecting the required elements from the three according to Purpose and Concern.

Connecting that position to development work yields the following four uses. This list is not a competing structure for the article; it is a map showing where the terms, correspondences, semantic structure, retrieval, access, and approval explained below are useful.

What the four uses share is that generative AI traces correspondence among terms, Model Elements, documents, rules, and evidence rather than relying on text fragments alone. Its results are candidates, not authoritative updates. People examine meaning, source authority, scope, and currency, and reflect only approved updates in the Glossary, documents, and Models. The following sections first explain this shared foundation, then return to the four uses.

Here, a term means a term defined in the Glossary. The Glossary manages its name, meaning, boundary, and synonyms, and internally refers to each term by a stable ID. Using a defined Glossary term as the entry point for correspondence, rather than a display label or string similarity, distinguishes different meanings of the same expression and keeps the same concept traceable after a name changes—both when reviewing a Domain Model and when answering a Domain inquiry.

Object Model elements, Knowledge Model nodes, and Literate Model descriptions can be mapped to the same Glossary term. For example, “state” can denote a business state, a user-interface state, or processing progress. The Glossary defines their boundaries, and the Knowledge Model retains relationships to documents, StateMachines, Events, and business rules so generative AI can determine which state it is explaining.

Knowledge Modeling does more than split text into searchable fragments. For defining Models through CML, reviewing a Domain Model, creating and reviewing a Use Case Model, and answering inquiries for Domain understanding, it enables generative AI and people to examine which references denote the same concept, which are different, how they relate, and what evidence supports them.

A rule represented in a Knowledge Model does not become executable merely by being represented there. Executable structure belongs to the Object Model and CML. The Knowledge Model retains the rule’s meaning, classification, evidence, and relationships to relevant sources and Model Elements in a form generative AI can explore.

CML is the language used to express an Object Model executably. The Knowledge Model does not replace CML. By connecting terms, rules, relationships, and evidence, it supplies the context through which generative AI can explain a proposed change and people can decide whether it should be reflected in CML.

When reviewing a Domain Model, its structures, constraints, and terms are checked against definitions, rules, and evidence. If the Knowledge Model retains those correspondences, generative AI can show inconsistency candidates and their evidence. People judge whether the candidates are valid in the Domain’s meaning.

For a Use Case Model, the vocabulary, purposes, and business rules of Scenarios in the Literate Model are organized. The Knowledge Model maps them to executable elements such as Services, Operations, Events, and StateMachines. This makes it possible to check, both while creating and reviewing, whether a Scenario’s meaning is preserved in the executable Model.

Distinguishing Knowledge Model content from retrieval and delivery mechanisms clarifies the role of each technology. They are not consecutive stages of a one-way process; they have distinct responsibilities.

RDF represents information as subject-predicate-object triples, and an Ontology defines the semantic structure of the concepts, classifications, relationships, and constraints in use. A Knowledge Graph connects terms, documents, Model Elements, and sources through explicit relationships. Similarity from Embeddings and results retrieved by RAG are useful for finding related candidates, but do not determine concept identity or evidence validity.

Consider “order confirmation,” the example introduced in Part 5, from the Knowledge Model side. The Object Model contains formal structures such as Order, inventory reservation, payment authorization, the order-confirmed Event, and a StateMachine. The Literate Model contains the “confirm an order” Use Case Scenario, exceptions, and decision rationale.

The Knowledge Model retains correspondences from the Glossary-defined term “order confirmation” to the Use Case Scenario, StateMachine, Event, business rules, and evidence and sources. This allows generative AI to explain what is required, what happens on success, and why each condition is needed from one concept.

Generative AI can find related candidates across many documents and Models, follow relationships, and produce candidate explanations. The existence of a search result, however, is different from its validity for the current subject.

A Knowledge Model relates claims to sources, scope, versions, update times, and approval records where available. Generative AI shows the basis of an explanation, while people examine business meaning, source authority, applicability, and currency. A relationship found by AI remains a candidate until approved; only approved relationships and evidence are returned to authoritative sources.

The four uses are not independent phases. Domain Modeling traces meaning, structure, rules, and boundaries; Application Modeling explains and checks correspondence between Use Case Scenarios and executable elements such as Services, Operations, Events, and StateMachines. Organizing the same terms, rules, evidence, and correspondences makes them consistently usable for CML updates, reviews, and inquiries.

The BoK provides SmartDox documents, Glossary entries, Models, and metadata that expresses their meaning as knowledge resources usable by generative AI. Knowledge Modeling organizes what is included in those resources and with which meanings and evidence. RAG and MCP deliver selected knowledge to questions and work.

This article organized the Knowledge Model as a constituent through which generative AI explores concepts, relationships, rules, and evidence and explains them to people. The next article turns to Literate Modeling and examines how subjects, purposes, situations, requirements, decisions, scenarios, intent, assumptions, and history are organized as a Literate Model shared by people and generative AI, primarily through natural language.