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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 ...
A knowledge graph = {,,} is a collection of entities , relations , and facts . [5] A fact is a triple (,,) that denotes a link between the head and the tail of the triple. . Another notation that is often used in the literature to represent a triple (or fact) is <,, >
The other extreme is that the content of each sentence in a documents is converted in the formal knowledge representation language and thus the objects that are mentioned in those sentences become an integral part of the computer interpretable knowledge model. For example, the knowledge that the API 617 standard contains a standard ...
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 ...
Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred. [18] [19] In 2012, Google gave their knowledge graph the name Knowledge Graph. The semantic link network was systematically studied as a semantic social networking method. Its basic model consists of semantic nodes, semantic links between ...
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.
Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources.The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing.