An example of a semantic network is "WordNet, a "lexical database of "English. It groups English words into sets of synonyms called "synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are "meronymy (A is part of B, i.e. B has A as a part of itself), "holonymy (B is part of A, i.e. A has B as a part of itself), "hyponymy (or "troponymy) (A is subordinate of B; A is kind of B), "hypernymy (A is superordinate of B), "synonymy (A denotes the same as B) and "antonymy (A denotes the opposite of B).
WordNet properties have been studied from a "network theory perspective and compared to other semantic networks created from "Roget's Thesaurus and "word association tasks. From this perspective the three of them are a "small world structure.
It is also possible to represent logical descriptions using semantic networks such as the "existential graphs of "Charles Sanders Peirce or the related "conceptual graphs of "John F. Sowa. These have expressive power equal to or exceeding standard "first-order predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing.
Other examples of semantic networks are "Gellish models. "Gellish English with its "Gellish English dictionary, is a "formal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable.
"SciCrunch is a collaboratively edited knowledge base for scientific resources. It provides unambiguous identifiers (Research Resource IDentifiers or RRIDs) for software, lab tools etc. and it also provides options to create links between RRIDs and from communities.
Another example of semantic networks, based on "category theory, is "ologs. Here each type is an object, representing a set of things, and each arrow is a morphism, representing a function. "Commutative diagrams also are prescribed to constrain the semantics.
In the social sciences people sometimes use the term semantic network to refer to "co-occurrence networks. The basic idea is that words that co-occur in a unit of text, e.g. a sentence, are semantically related to one another. Ties based on co-occurrence can then be used to construct semantic networks.
There are also elaborate types of semantic networks connected with corresponding sets of software tools used for "lexical "knowledge engineering, like the Semantic Network Processing System ("SNePS) of Stuart C. Shapiro or the "MultiNet paradigm of Hermann Helbig, especially suited for the semantic representation of natural language expressions and used in several "NLP applications.
Semantic networks are used in specialized information retrieval tasks, such as "plagiarism detection. They provide information on hierarchical relations in order to employ "semantic compression to reduce language diversity and enable the system to match word meanings, independently from sets of words used.
The first semantic network for computers was Nude, created by R. H. Richens of the Cambridge Language Research Unit in 1956 as an interlingua for machine translation of natural languages.
usage [of the term 'knowledge graph'] has evolved
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