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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.
In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
Key features of GBKR, the graph-based knowledge representation and reasoning model developed by Chein and Mugnier and the Montpellier group, can be summarized as follows: [3] All kinds of knowledge (ontology, rules, constraints and facts) are labeled graphs, which provide an intuitive and easily understandable means to represent knowledge.
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 ...
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 ...
SNePS is a knowledge representation, reasoning, and acting (KRRA) system developed and maintained by Stuart C. Shapiro and colleagues at the State University of New York at Buffalo. SNePS is simultaneously a logic-based, frame-based, and network-based KRRA system. It uses an assertional model of knowledge, in that a SNePS knowledge base (KB ...
A standard representation of the pyramid form of DIKW models, from 2007 and earlier. [1] [2]The DIKW pyramid, also known variously as the knowledge pyramid, knowledge hierarchy, information hierarchy, [1]: 163 DIKW hierarchy, wisdom hierarchy, data pyramid, and information pyramid, [citation needed] sometimes also stylized as a chain, [3]: 15 [4] refer to models of possible structural and ...