Editing Semantic network (section)

=== Other examples ===
It is also possible to represent logical descriptions using semantic networks such as the [[existential graph]]s of [[Charles Sanders Peirce]] or the related [[conceptual graph]]s of [[John F. Sowa]].<ref name='Sowa'>{{cite encyclopedia
|author=John F. Sowa
|editor=Stuart C Shapiro
|encyclopedia=Encyclopedia of Artificial Intelligence
|title=Semantic Networks
|url=http://www.jfsowa.com/pubs/semnet.htm
|access-date=2008-04-29
|year=1987
|author-link=John F. Sowa
|archive-date=8 October 2018
|archive-url=https://web.archive.org/web/20181008185537/http://www.jfsowa.com/pubs/semnet.htm
|url-status=live
}}</ref> These have expressive power equal to or exceeding standard [[first-order predicate calculus|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 [[olog]]s. 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]].<ref name='Atteveldt'>{{cite book |author=Wouter Van Atteveldt |title=Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content |publisher=BookSurge Publishing |url=http://vanatteveldt.com/wp-content/uploads/vanatteveldt_semanticnetworkanalysis.pdf |year=2008 |access-date=28 November 2021 |archive-date=28 November 2021 |archive-url=https://web.archive.org/web/20211128205957/http://vanatteveldt.com/wp-content/uploads/vanatteveldt_semanticnetworkanalysis.pdf |url-status=live }}</ref><ref>{{cite journal |last1=Segev |first1=Elad |title=Textual network analysis: Detecting prevailing themes and biases in international news and social media |journal=Sociology Compass |date=2020 |volume=14 |issue=4 |doi=10.1111/soc4.12779 |s2cid=212890998 |url=https://onlinelibrary.wiley.com/doi/full/10.1111/soc4.12779 |access-date=5 December 2021 |archive-date=5 December 2021 |archive-url=https://web.archive.org/web/20211205140727/https://onlinelibrary.wiley.com/doi/full/10.1111/soc4.12779 |url-status=live |url-access=subscription }}</ref> 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. This process includes identifying keywords in the text, constructing co-occurrence networks, and analyzing the networks to find central words and clusters of themes in the network. It is a particularly useful method to analyze large text and [[big data]].<ref>{{cite book |last1=Segev |first1=Elad |title=Semantic Network Analysis in Social Sciences |date=2022 |publisher=Routledge |location=London |isbn=9780367636524 |url=https://www.routledge.com/Semantic-Network-Analysis-in-Social-Sciences/Segev/p/book/9780367636524 |access-date=5 December 2021 |archive-date=5 December 2021 |archive-url=https://web.archive.org/web/20211205140726/https://www.routledge.com/Semantic-Network-Analysis-in-Social-Sciences/Segev/p/book/9780367636524 |url-status=live }}</ref>