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{{short description|Knowledge base that represents semantic relations between concepts in a network}}
{{Use dmy dates|date=January 2021}}
[[Image:Semantic Net.svg|thumb|upright|Example of a semantic network]]
{{Network science}}
{{InfoMaps}}
A '''semantic network''', or '''frame network''' is a [[knowledge base]] that represents [[Semantics|semantic]] relations between [[concept]]s in a network. This is often used as a form of [[Knowledge representation and reasoning|knowledge representation]]. It is a [[directed graph|directed]] or [[undirected graph]] consisting of [[vertex (graph theory)|vertices]], which represent [[concept]]s, and [[graph theory|edges]], which represent [[semantic relationship|semantic relations]] between concepts,<ref name = 'Sowa'/> mapping or connecting [[semantic field]]s. A semantic network may be instantiated as, for example, a [[graph database]] or a [[concept map]]. Typical standardized semantic networks are expressed as [[semantic triple]]s.
Semantic networks are used in [[natural language processing]] applications such as [[semantic parsing]]<ref>Poon, Hoifung, and Pedro Domingos. "[https://aclanthology.info/pdf/D/D09/D09-1001.pdf Unsupervised semantic parsing]." Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, 2009.</ref> and [[word-sense disambiguation]].<ref>Sussna, Michael. "[https://sites.ualberta.ca/~golmoham/SW/ontology%20based%20similarity/Word%20sense%20disambiguation%20for%20free-text%20indexing%20using%20a%20massive%20semantic%20network.pdf Word sense disambiguation for free-text indexing using a massive semantic network]." Proceedings of the second international conference on Information and knowledge management. ACM, 1993.</ref> Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field.
== History ==
Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries. The earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD.
In computing history, "Semantic Nets" for the propositional calculus were first implemented for [[computers]] by [[Richard H. Richens]] of the Cambridge Language Research Unit in 1956 as an "[[Pivot language|interlingua]]" for [[machine translation]] of [[natural language]]s.<ref>{{cite book |editor1-last=Lehmann |editor1-first=Fritz |editor2-last=Rodin |editor2-first=Ervin Y. |date=1992 |title=Semantic networks in artificial intelligence |series=International series in modern applied mathematics and computer science |volume=24 |___location=Oxford; New York |publisher=[[Pergamon Press]] |isbn=978-0080420127 |oclc=26391254 |page=6 |quote=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.}}</ref> Although the importance of this work and the CLRU was only belatedly realized.
Semantic networks were also independently implemented by Robert F. Simmons<ref name='Simmons1963'>{{cite journal | title=Synthetic language behavior | journal=Data Processing Management | year=1963 | last=Robert F. Simmons |volume=5 |issue=12 |pages=11–18}}</ref> and Sheldon Klein, using the first order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association [Association for Computational Linguistics], Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962-1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text."<ref>Simmons, [https://www.aclweb.org/anthology/P82-1022 "Themes From 1972"], ''ACL Anthology'', 1982</ref> Other researchers, most notably [[M. Ross Quillian]]<ref name='Quillian1963'>Quillian, R. A notation for representing conceptual information: An application to semantics and mechanical English para- phrasing. SP-1395, System Development Corporation, Santa Monica, 1963.</ref> and others at [[System Development Corporation]] helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's from these publications at SDC that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by [[Allan M. Collins]] and Quillian (e.g., Collins and Quillian;<ref name='Collins1969'>{{cite journal | title=Retrieval time from semantic memory | journal=Journal of Verbal Learning and Verbal Behavior | year=1969 | last1=Allan M. Collins |author2= M. R. Quillian |volume=8 |issue=2 |pages=240–247 |doi=10.1016/S0022-5371(69)80069-1 }}</ref><ref name='Collins1970'>{{cite journal |title=Does category size affect categorization time? |journal=Journal of Verbal Learning and Verbal Behavior |year=1970 |last=Allan M. Collins|author2=M. Ross Quillian |volume=9 |issue=4 |pages=432–438 |doi=10.1016/S0022-5371(70)80084-6 }}</ref> Collins and Loftus<ref name='Collins1975'>{{cite journal |title=A spreading-activation theory of semantic processing |journal=Psychological Review |year=1975 |last=Allan M. Collins |author2=Elizabeth F. Loftus |volume=82 |issue=6 | doi = 10.1037/0033-295x.82.6.407 |pages=407–428|s2cid=14217893 }}</ref> Quillian<ref>Quillian, M. R. (1967). Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science, 12(5), 410–430.</ref><ref>Quillian, M. R. (1968). Semantic memory. Semantic information processing, 227–270.</ref><ref>{{cite journal | last1 = Quillian | first1 = M. R. | year = 1969 | title = The teachable language comprehender: a simulation program and theory of language | journal = Communications of the ACM | volume = 12 | issue = 8| pages = 459–476 | doi=10.1145/363196.363214| s2cid = 15304609 }}</ref><ref>Quillian, R. Semantic Memory. Unpublished doctoral dissertation, Carnegie Institute of Technology, 1966.</ref>). Still later in 2006, Hermann Helbig fully described [[MultiNet]].<ref>{{cite book | last1 = Helbig | first1 = H. | year = 2006 | title = Knowledge Representation and the Semantics of Natural Language | url = http://14.139.206.50:8080/jspui/bitstream/1/2412/1/Helbig,%20Hermann%20-%20Knowledge%20Representation%20and%20the%20Semantics%20of%20Natural%20Language.pdf | isbn=978-3540244615}}</ref>
In the late 1980s, two [[Netherlands]] universities, [[University of Groningen|Groningen]] and [[University of Twente|Twente]], jointly began a project called ''Knowledge Graphs'', which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph.<ref>{{cite book |last=Van de Riet |first=R. P. |date=1992 |title=Linguistic Instruments in Knowledge Engineering |url=http://www.stokman.org/artikel/92Jame.KnowGraphs.LIKE.pdf |publisher=Elsevier Science Publishers |page=98 |isbn=978-0444883940}}</ref> In the subsequent decades, the distinction between semantic networks and [[knowledge graph]]s was blurred.<ref>{{cite conference |url=https://books.google.com/books?id=15PDCgAAQBAJ&pg=PA444 |title=Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation |last1=Hulpus |first1=Ioana |last2=Prangnawarat |first2=Narumol |date=2015 |publisher=Springer International Publishing |book-title=The Semantic Web – ISWC 2015: 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11–15, 2015, Proceedings, Part 1 |pages=444 |isbn=9783319250076 |conference=[[International Semantic Web Conference]] 2015}}</ref><ref>{{cite web |url=https://www.authorea.com/users/6341/articles/107281 |title=What is a Knowledge Graph? |last1=McCusker |first1=James P. |last2=Chastain |first2=Katherine |date=April 2016 |website=authorea.com |access-date=15 June 2016 |quote="usage [of the term 'knowledge graph'] has evolved"}}</ref> In 2012, [[Google]] gave their knowledge graph the name [[Knowledge Graph (Google)|Knowledge Graph]].
The Semantic Link Network was systematically studied as a social semantics networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004.<ref>H. Zhuge, Knowledge Grid, World Scientific Publishing Co. 2004.</ref> This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998<ref>H. Zhuge, Inheritance rules for flexible model retrieval. Decision Support Systems 22(4)(1998)379–390</ref> and the Active Document Framework ADF.<ref>H. Zhuge, Active e-document framework ADF: model and tool. Information & Management 41(1): 87–97 (2003)</ref> Since 2003, research has developed toward social semantic networking.<ref>H.Zhuge and L.Zheng, Ranking Semantic-linked Network, WWW 2003</ref> This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network).<ref>H.Zhuge, The Semantic Link Network, in The Knowledge Grid: Toward Cyber-Physical Society, World Scientific Publishing Co. 2012.</ref> The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network.<ref>H. Zhuge, L. Zheng, N. Zhang and X. Li, An automatic semantic relationships discovery approach. WWW 2004: 278–279.</ref><ref>H. Zhuge, Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning, IEEE Transactions on Knowledge and Data Engineering, 21(6)(2009)785–799.</ref> Recently it has been developed to support Cyber-Physical-Social Intelligence.<ref>H.Zhuge, Semantic linking through spaces for cyber-physical-socio intelligence: A methodology, Artificial Intelligence, 175(2011)988–1019.</ref> It was used for creating a general summarization method.<ref>H. Zhuge, Multi-Dimensional Summarization in Cyber-Physical Society, Morgan Kaufmann, 2016.</ref> The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links<ref>H. Zhuge, The Web Resource Space Model, Springer, 2008.</ref><ref>H.Zhuge and Y.Xing, Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society, IEEE Transactions on Service Computing, 5(3)(2012)404–421.</ref> It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications.<ref>{{cite journal | doi=10.1109/ACCESS.2018.2856530 | title=Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network | date=2018 | last1=Sun | first1=Xiaoping | last2=Zhuge | first2=Hai | journal=IEEE Access | volume=6 | pages=40611–40625 | bibcode=2018IEEEA...640611S }}</ref><ref>{{cite journal | doi=10.1371/journal.pone.0199303 | doi-access=free | title=The contribution of cause-effect link to representing the core of scientific paper—The role of Semantic Link Network | date=2018 | last1=Cao | first1=Mengyun | last2=Sun | first2=Xiaoping | last3=Zhuge | first3=Hai | journal=PLOS ONE | volume=13 | issue=6 | pages=e0199303 | pmid=29928017 | bibcode=2018PLoSO..1399303C | pmc=6013162 }}</ref> Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence<ref>H. Zhuge, Cyber-Physical-Social Intelligence on Human-Machine-Nature Symbiosis, Springer, 2020.</ref>
More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the [[semantic similarity network|Semantic Similarity Network]] (SSN) that contains specialized relationships and propagation algorithms to simplify the [[semantic similarity]] representation and calculations.<ref>{{cite book |last=Bendeck |first=Fawsy |date=2008 |title=WSM-P workflow semantic matching platform |___location=München |publisher=Verl. Dr. Hut |isbn=9783899638547 |oclc=501314022}}</ref>
== Basics of semantic networks ==
A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another.
Most semantic networks are cognitively based. They also consist of arcs and nodes which can be organized into a taxonomic hierarchy. Semantic networks contributed ideas of [[spreading activation]], [[Inheritance (object-oriented programming)|inheritance]], and nodes as proto-objects.
== Examples ==
=== In Lisp ===
The following code shows an example of a semantic network in the [[Lisp (programming language)|Lisp programming language]] using an [[association list]].
<syntaxhighlight lang="lisp">
(setq *database*
'((canary (is-a bird)
(color yellow)
(size small))
(penguin (is-a bird)
(movement swim))
(bird (is-a vertebrate)
(has-part wings)
(reproduction egg-laying))))
</syntaxhighlight>
To extract all the information about the "canary" type, one would use the <code>assoc</code> function with a key of "canary".<ref>{{cite web|last=Swigger|first=Kathleen|title=Semantic.ppt|url=http://zeus.csci.unt.edu/swigger/csci3210/semantic.ppt|access-date=23 March 2011}}</ref>
=== WordNet ===
{{Main|WordNet}}
An example of a semantic network is [[WordNet]], a [[lexicon|lexical]] database of [[English language|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 a meronym of B if A is part of B), [[holonymy]] (B is a holonym of A if B contains A), [[hyponym]]y (or [[troponymy]]) (A is subordinate of B; A is kind of B), [[hypernym]]y (A is superordinate of B), [[synonym]]y (A denotes the same as B) and [[antonym]]y (A denotes the opposite of B).
WordNet properties have been studied from a [[Graph theory|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 network|small world structure]].<ref name=Steyvers2005>{{cite journal
| author = Steyvers, M.
|author2=Tenenbaum, J.B.
| year = 2005
| title = The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
| journal = Cognitive Science
| volume = 29
| issue = 1
| pages = 41–78
| doi = 10.1207/s15516709cog2901_3
|pmid=21702767
| arxiv = cond-mat/0110012
|s2cid=6000627
}}</ref>
=== 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}}</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}}</ref>
== 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>[http://www.cse.buffalo.edu/~shapiro/ Stuart C. Shapiro]</ref> or the [[MultiNet]] paradigm of Hermann Helbig,<ref>[http://pi7.fernuni-hagen.de/helbig/index_en.html Hermann Helbig]</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.}}</ref> ([[Conference on Neural Information Processing Systems|NIPS]] 2013). Applications of embedding knowledge base data include [[Social network analysis]] and [[Relationship extraction]].
== See also ==
{{Div col|colwidth=20em}}
* [[Abstract semantic graph]]
* [[Chunking (psychology)]]
* [[CmapTools]]
* [[Concept map]]
* [[Network diagram]]
* [[Ontology (information science)]]
* [[Repertory grid]]
* [[Semantic lexicon]]
* [[Semantic similarity network]]
* [[Semantic neural network]]
* [[SemEval]] – an ongoing series of evaluations of [[Semantic analysis (computational)|computational semantic analysis]] systems
* [[Sparse distributed memory]]
* [[Taxonomy (general)]]
* [[Unified Medical Language System]] (UMLS)
* [[Word-sense disambiguation]] (WSD)
* [[Resource Description Framework]]
{{Div col end}}
=== Other examples ===
* [[Cognition Network Technology]]
* [[Lexipedia]]
* [[OpenCog]]
* [[Open Mind Common Sense]] (OMCS)
* [[Schema.org]]
* [[SNOMED CT]]
* [[Universal Networking Language]] (UNL)
* [[Wikidata]]
* [[Freebase (database)|Freebase]]
== References ==
{{reflist|30em}}
== Further reading ==
* Allen, J. and A. Frisch (1982). "[http://www.aclweb.org/anthology/P82-1004 What's in a Semantic Network]". In: ''Proceedings of the 20th. annual meeting of ACL'', Toronto, pp. 19–27.
* John F. Sowa, Alexander Borgida (1991). ''[https://books.google.com/books?id=lTKnCQAAQBAJ&printsec=frontcover#v=onepage&q&f=false Principles of Semantic Networks: Explorations in the Representation of Knowledge]''.
== External links ==
{{Commons category|Semantic networks}}
* [http://www.jfsowa.com/pubs/semnet.htm "Semantic Networks"] by John F. Sowa
* [https://web.archive.org/web/20091218102540/http://www.knowledgegrid.net/~h.zhuge/SLN.htm "Semantic Link Network" ] by Hai Zhuge
{{Semantic Web}}
{{Natural language processing}}
{{Authority control}}
[[Category:Knowledge representation]]
[[Category:Networks]]
[[Category:Semantic relations]]
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