semantic network

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Example of a semantic network

Semantic networkFrom Wikipedia, the free encyclopedia

A semantic network, or frame network, is a network which represents semantic relations betweenconcepts. This is often used as a form of knowledge representation. It is a directed or undirected graphconsisting of vertices, which represent concepts, and edges.[1]

Contents1 History2 Basics of semantic networks

2.1 Limitations3 Examples

3.1 Semantic Net in Lisp3.2 WordNet3.3 Other examples

4 Software tools5 See also

5.1 Examples6 References7 Further reading8 External links

HistoryIn 1909, Charles S. Peirce proposed a graphicalnotation of nodes and edges called "existentialgraphs" that he called "the logic of the future". Thisbegan the debate between advocates of "logic" andadvocates of "semantic networks." This debateobscured the fact that semantics networks, at leastthose with well-defined semantics, are a form oflogic.[2]

"Semantic Nets" were first invented for computers byRichard H. Richens of the Cambridge LanguageResearch Unit in 1956 as an "interlingua" formachine translation of natural languages.

They were developed by Robert F. Simmons[3] and M. Ross Quillian[4] at System Development Corporationin the early 1960s. It later featured prominently in the work of Allan M. Collins and Quillian (e.g., Collinsand Quillian;[5][6] Collins and Loftus[7] Quillian[8][9][10][11])



Basics of semantic networksA semantic network is used when one has knowledge that is best understood as a set of concepts that arerelated to one another.

Most semantic networks are cognitively based. They also consist of arcs and nodes which can be organizedinto a taxonomic hierarchy. Semantic networks contributed ideas of spreading activation, inheritance, andnodes as proto-objects.

LimitationsSemantic networks are intractable for large domains, and they do not represent performance or meta-knowledge very well.

Some properties are not easily expressed using a semantic network, e.g., negation, disjunction, and generalnon-taxonomic knowledge. Expressing these relationships requires workarounds, such as havingcomplementary predicates and using specialized procedures to check for them, but this can be regarded asless elegant.

ExamplesSemantic Net in LispUsing an association list.

You would use the "assoc" function with a key of "canary" to extract all the information about the "canary"type.[12]

WordNetAn example of a semantic network is WordNet, a lexical database of English. It groups English words intosets of synonyms called synsets, provides short, general definitions, and records the various semanticrelations 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).

(defun *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))))



WordNet properties have been studied from a network theory perspective and compared to other semanticnetworks created from Roget's Thesaurus and word association tasks. From this perspective the three ofthem are a small world structure.[13]

Other examplesIt is also possible to represent logical descriptions using semantic networks such as the existential Graphs ofCharles Sanders Peirce or the related Conceptual Graphs of John F. Sowa.[1] These have expressive powerequal to or exceeding standard first-order predicate logic. Unlike WordNet or other lexical or browsingnetworks, semantic networks using these representations can be used for reliable automated logicaldeduction. 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 Englishdictionary, is a formal language that is defined as a network of relations between concepts and names ofconcepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset ofDutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledgemodels 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 bya relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Eachrelated thing is either a concept or an individual thing that is classified by a concept. The definitions ofconcepts are created in the form of definition models (definition networks) that together form a GellishDictionary. A Gellish network can be documented in a Gellish database and is computer interpretable.

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 diagramsalso are prescribed to constrain the semantics.

Software toolsThere are also elaborate types of semantic networks connected with corresponding sets of software toolsused for lexical knowledge engineering, like the Semantic Network Processing System (SNePS) of Stuart C.Shapiro[14] or the MultiNet paradigm of Hermann Helbig,[15] especially suited for the semanticrepresentation of natural language expressions and used in several NLP applications.

Semantic networks are used in specialized information retrieval tasks, such as plagiarism detection. Theyprovide information on hierarchical relations in order to employ semantic compression to reduce languagediversity and enable the system to match word meanings, independently from sets of words used.

See alsoAbstract semantic graphConceptual graphKnowledge representationMind mapNetwork diagramOntologySemantic lexicon



Semantic WebSemantic computingSemantic neural networkSemEval - an ongoing series of evaluations of computational semantic analysis systemsSparse distributed memoryTaxonomy (general)Unified Medical Language System (UMLS)Word sense disambiguation (WSD)

ExamplesLexipediaWordNetSNOMED CTConceptNetUniversal Networking LanguageExpert SystemMicrosoft Probase [16]

References1. John F. Sowa (1987). "Semantic Networks" (http://www.jfsowa.com/pubs/semnet.htm). In Stuart C Shapiro.

Encyclopedia of Artificial Intelligence. Retrieved 2008-04-29.2. Russell, Stuart J.; Norvig, Peter (2010). Artificial intelligence : a modern approach (3rd ed.). Upper Saddle River,

N.J.: Prentice Hall. p. 454. ISBN 978-0-13-604259-4.3. Robert F. Simmons (1963). "Synthetic language behavior". Data Processing Management 5 (12): 1118.4. Quillian, R. A notation for representing conceptual information: An application to semantics and mechanical English

para- phrasing. SP-1395, System Development Corporation, Santa Monica, 1963.5. Allan M. Collins; M. R. Quillian (1969). "Retrieval time from semantic memory". Journal of verbal learning and

verbal behavior 8 (2): 240247. doi:10.1016/S0022-5371(69)80069-1 (https://dx.doi.org/10.1016%2FS0022-5371%2869%2980069-1).

6. Allan M. Collins; M. Ross Quillian (1970). "Does category size affect categorization time?". Journal of verballearning and verbal behavior 9 (4): 432438. doi:10.1016/S0022-5371(70)80084-6(https://dx.doi.org/10.1016%2FS0022-5371%2870%2980084-6).

7. Allan M. Collins; Elizabeth F. Loftus (1975). "A spreading-activation theory of semantic processing". PsychologicalReview 8.

8. Quillian, M. R. (1967). Word concepts: A theory and simulation of some basic semantic capabilities. BehavioralScience, 12(5), 410-430.

9. Quillian, M. R. (1968). Semantic memory. Semantic information processing, 227270.10. Quillian, M. R. (1969). The teachable language comprehender: a simulation program and theory of language.

Communications of the ACM, 12(8), 459-476.11. Quillian, R. Semantic Memory. Unpublished doctoral dissertation, Carnegie Institute of Technology, 1966.12. Swigger, Kathleen. "Semantic.ppt" (http://zeus.csci.unt.edu/swigger/csci3210/semantic.ppt). Retrieved 23 March

2011.13. Steyvers, M.; Tenenbaum, J.B. (2005). "The Large-Scale Structure of Semantic Networks: Statistical Analyses and a

Model of Semantic Growth". Cognitive Science 29 (1): 4178. doi:10.1207/s15516709cog2901_3(https://dx.doi.org/10.1207%2Fs15516709cog2901_3).

14. Stuart C. Shapiro (http://www.cse.buffalo.edu/~shapiro/)15. Hermann Helbig (http://pi7.fernuni-hagen.de/helbig/index_en.html)16. Wu, Wentao, et al. "Probase: A probabilistic taxonomy for text understanding." Proceedings of the 2012 ACM

SIGMOD International Conference on Management of Data. ACM, 2012.



Wikimedia Commons hasmedia related to Semanticnetworks.

Further readingAllen, J. and A. Frisch (1982). "What's in a Semantic Network". In: Proceedings of the 20th. annualmeeting of ACL, Toronto, pp. 19-27.John F. Sowa, Alexander Borgida (1991). Principles of Semantic Networks: Explorations in theRepresentation of Knowledge.

External links"Semantic Networks"(http://www.jfsowa.com/pubs/semnet.htm) by John F. Sowa"Semantic Link Network"(http://www.knowledgegrid.net/~H.Zhuge/SLN.htm) by HaiZhuge

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