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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 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 ...
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.
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 ...
Reification allows the representation of assertions so that they can be referred to or qualified by other assertions, i.e., meta-knowledge. [ 3 ] The message "John is six feet tall" is an assertion involving truth that commits the speaker to its factuality, whereas the reified statement "Mary reports that John is six feet tall" defers such ...
Sowa's research interests since the 1970s were in the field of artificial intelligence, expert systems and database query linked to natural languages. [4] In his work he combines ideas from numerous disciplines and eras modern and ancient, for example, applying ideas from Aristotle, the medieval scholastics to Alfred North Whitehead and including database schema theory, and incorporating the ...
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.
In knowledge representation, a class is a collection of individuals or individuals objects. [1] A class can be defined either by extension (specifying members), or by intension (specifying conditions), using what is called in some ontology languages like OWL.