Embedded HTML5 Microdata Statement 98
In computer science and information science, an ontology or knowledge graph formally represents knowledge as a hierarchy of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts. These interrelationships distinguish an ontology from a taxonomy, which only builds a hierarchical order without interrelationships between the individual concepts. Ontologies /knowledge graphs are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework.
Knowledge graphs enable the representation of knowledge: humans usually understand the correct meaning of a term, thanks to their background knowledge and the context in which a specific term is used. A machine lacks this ability, naturally. It can, however “learn” about the semantic meaning of a term. Often, so-called Conceptual Graphs are used to depict this meaning: concepts are linked to each other through different relations. Through the relations that have been set and the location of the term in the ontology the meaning of a specific term becomes interpretable for a machine.
Ontology / Knowledge Graph
In computer science and information science, an ontology or knowledge graph formally represents knowledge as a hierarchy of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts. These interrelationships distinguish an ontology from a taxonomy, which only builds a hierarchical order without interrelationships between the individual concepts. Ontologies /knowledge graphs are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture as a form of knowledge representation about the world or some part of it. The creation of domain ontologies is also fundamental to the definition and use of an enterprise architecture framework.
Knowledge graphs enable the representation of knowledge: humans usually understand the correct meaning of a term, thanks to their background knowledge and the context in which a specific term is used. A machine lacks this ability, naturally. It can, however “learn” about the semantic meaning of a term. Often, so-called Conceptual Graphs are used to depict this meaning: concepts are linked to each other through different relations. Through the relations that have been set and the location of the term in the ontology the meaning of a specific term becomes interpretable for a machine.
Embedded HTML5 Microdata Statement 97
Ontology / Knowledge Graph
Embedded HTML5 Microdata Statement 99