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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.
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]
A frame language is a technology used for knowledge representation in artificial intelligence. They are similar to class hierarchies in object-oriented languages although their fundamental design goals are different. Frames are focused on explicit and intuitive representation of knowledge whereas objects focus on encapsulation and information ...
Other approaches include the use of automated theorem proving, logic programming, blackboard systems, and term rewriting systems such as Constraint Handling Rules (CHR). These more formal approaches are covered in detail in the Wikipedia article on knowledge representation and reasoning.
Multiperspectivity (sometimes polyperspectivity) is a characteristic of narration or representation, where more than one perspective is represented to the audience. [1]Most frequently the term is applied to fiction which employs multiple narrators, often in opposition to each-other or to illuminate different elements of a plot, [1] creating what is sometimes called a multiple narrative, [2] [3 ...
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]
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 ...
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 ...