Editing Semantic network (section)

== Software tools ==
There are also elaborate types of semantic networks connected with corresponding sets of software tools used for [[Lexicon|lexical]] [[knowledge engineering]], like the Semantic Network Processing System ([[SNePS]]) of Stuart C. Shapiro<ref>{{cite web| url = http://www.cse.buffalo.edu/~shapiro/| title = Stuart C. Shapiro| access-date = 29 August 2006| archive-date = 27 August 2006| archive-url = https://web.archive.org/web/20060827101751/http://www.cse.buffalo.edu/~shapiro/| url-status = live}}</ref> or the [[MultiNet]] paradigm of Hermann Helbig,<ref>{{cite web| url = http://pi7.fernuni-hagen.de/helbig/index_en.html| title = Hermann Helbig| access-date = 14 March 2006| archive-date = 4 May 2006| archive-url = https://web.archive.org/web/20060504090936/http://pi7.fernuni-hagen.de/helbig/index_en.html| url-status = live}}</ref> especially suited for the semantic representation of natural language expressions and used in several [[Natural language processing|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.

[[Google_Knowledge_Graph|The Knowledge Graph]] proposed by Google in 2012 is actually an application of semantic network in search engine.

Modeling multi-relational data like semantic networks in low-dimensional spaces through forms of [[embedding]] has benefits in expressing entity relationships as well as extracting relations from mediums like text. There are many approaches to learning these embeddings, notably using Bayesian clustering frameworks or energy-based frameworks, and more recently, TransE<ref>{{Citation|last1=Bordes|first1=Antoine|title=Translating Embeddings for Modeling Multi-relational Data|date=2013|url=http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf|work=Advances in Neural Information Processing Systems 26|pages=2787–2795|editor-last=Burges|editor-first=C. J. C.|publisher=Curran Associates, Inc.|access-date=2018-11-29|last2=Usunier|first2=Nicolas|last3=Garcia-Duran|first3=Alberto|last4=Weston|first4=Jason|last5=Yakhnenko|first5=Oksana|editor2-last=Bottou|editor2-first=L.|editor3-last=Welling|editor3-first=M.|editor4-last=Ghahramani|editor4-first=Z.|archive-date=20 December 2018|archive-url=https://web.archive.org/web/20181220220123/http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf|url-status=live}}</ref> ([[Conference on Neural Information Processing Systems|NeurIPS]] 2013). Applications of embedding knowledge base data include [[Social network analysis]] and [[Relationship extraction]].