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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 ...
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 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 ...
For example, knowledge about a compressor system includes that a compressor system consists of a compressor, a lubrication system, etc., whereas a lubrication system consists of a pump system, etc. Assume that this knowledge is expressed in a knowledge representation language that expresses knowledge as a collection of relations between two ...
Classification technology originally developed for Frame languages is a key enabler of the Semantic Web. [20] [21] The "neats vs. scruffies" divide also emerged in Semantic Web research, culminating in the creation of the Linking Open Data community—their focus was on exposing data on the Web rather than modeling.
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.
Aspects of ontology editors include: visual navigation possibilities within the knowledge model, inference engines and information extraction; support for modules; the import and export of foreign knowledge representation languages for ontology matching; and the support of meta-ontologies such as OWL-S, Dublin Core, etc. [30]
Knowledge discovery is an iterative and interactive process used to identify, analyze and visualize patterns in data. [1] Network analysis, link analysis and social network analysis are all methods of knowledge discovery, each a corresponding subset of the prior method. Most knowledge discovery methods follow these steps (at the highest level): [2]