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A concept model (a model of a concept) is quite different because in order to be a good model it need not have this real world correspondence. [3] In artificial intelligence, conceptual models and conceptual graphs are used for building expert systems and knowledge-based systems ; here the analysts are concerned to represent expert opinion on ...
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.
Knowledge representation and reasoning (KRR, KR&R, or KR²) is a field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog.
Class (knowledge representation) Closed-world assumption; Cognitive categorization; Cognitive map; Colon classification; Completeness (knowledge bases) Composite Capability/Preference Profiles; Composite portrait; Computer Science Ontology; Concept map; Concepticon; Conceptual graph; Conceptualization (information science) Consistency ...
KL-ONE (pronounced "kay ell won") is a knowledge representation system in the tradition of semantic networks and frames; that is, it is a frame language. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a structured inheritance network. [1] [2] [3]
Knowledge integration is the process of synthesizing multiple knowledge models (or representations) into a common model (representation).. Compared to information integration, which involves merging information having different schemas and representation models, knowledge integration focuses more on synthesizing the understanding of a given subject from different perspectives.
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 ...
All the different knowledge graph embedding models follow roughly the same procedure to learn the semantic meaning of the facts. [7] First of all, to learn an embedded representation of a knowledge graph, the embedding vectors of the entities and relations are initialized to random values. [7]