Semantic network

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

History

Example of a semantic network

In 1909, Charles S. Peirce proposed a graphical notation of nodes and edges called "existential graphs" that he called "the logic of the future". This began the debate between advocates of "logic" and advocates of "semantic networks." This debate obscured the fact that semantics networks, at least those with well-defined semantics, are a form of logic.[2]

"Semantic Nets" were first invented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages.

They were independently developed by Robert F. Simmons,[3] Sheldon Klein, Karen McConologue, M. Ross Quillian[4] and others at System Development Corporation in the early 1960s as part of the SYNTHEX project. It later featured prominently in the work of Allan M. Collins and Quillian (e.g., Collins and Quillian;[5][6] Collins and Loftus[7] Quillian[8][9][10][11])

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, and nodes as proto-objects.

Examples

Semantic Net in Lisp

Using an association list.

(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))))

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

WordNet

Main article: WordNet

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.[13]

Other examples

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.[1] 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.

Software tools

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[14] or the MultiNet paradigm of Hermann Helbig,[15] 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.

See also

Examples

References

  1. 1 2 John F. Sowa (1987). "Semantic Networks". 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): 11–18.
  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): 240–247. doi:10.1016/S0022-5371(69)80069-1.
  6. ↑ Allan M. Collins; M. Ross Quillian (1970). "Does category size affect categorization time?". Journal of verbal learning and verbal behavior 9 (4): 432–438. doi:10.1016/S0022-5371(70)80084-6.
  7. ↑ Allan M. Collins; Elizabeth F. Loftus (1975). "A spreading-activation theory of semantic processing". Psychological Review 82: 407–428. doi:10.1037/0033-295x.82.6.407.
  8. ↑ Quillian, M. R. (1967). Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science, 12(5), 410-430.
  9. ↑ Quillian, M. R. (1968). Semantic memory. Semantic information processing, 227–270.
  10. ↑ Quillian, M. R. (1969). "The teachable language comprehender: a simulation program and theory of language". Communications of the ACM 12 (8): 459–476. doi:10.1145/363196.363214.
  11. ↑ Quillian, R. Semantic Memory. Unpublished doctoral dissertation, Carnegie Institute of Technology, 1966.
  12. ↑ Swigger, Kathleen. "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): 41–78. doi:10.1207/s15516709cog2901_3.
  14. ↑ Stuart C. Shapiro
  15. ↑ Hermann Helbig
  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.

Further reading

External links

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