Semantic similarity network
In general, a semantic similarity network (SSN) is a form of semantic network.[1] SSN is specially designed to represent concepts and their relationships to calculate semantic distances. Bendeck (2004) introduces semantic similarity networks (SSN), as a specialization of semantic network to measure semantic distance from ontological representations.[2]
SSNs were first formally defined for computers by Fawsy Bendeck in his PhD thesis[3] as a directed graph[4] with concepts as nodes and relations as edges. The relations are grouped into relation types. The concepts and relations contain attribute values to evaluate the semantic similarity[5] between concepts. The relation types serves as templates (and taxonomy of relations) for relations containing attributes that are common to all relations of the same type.[6] An SSN is defined as:
Where C is a set of Concepts, R a set of relations between those concepts, and RT is a set of relation types.
References
- ^ R. H. Richens: "General program for mechanical translation between any two languages via an algebraic interlingua". Cambridge Language Research Unit. Mechanical Translation, November 1956; p. 37
- ^ Fawsy Bendeck, Three Fold "Ontology + Model + Instance (OMI) - Semantic Unification Process, In International Conference on Advances in Internet, Processing, System and Interdisciplinary Research (IPSI-2004), Stockholm, Sep 2004, ISBN 86-7466-1173.
- ^ Fawsy Bendeck, WSM-P Workflow Semantic Matching Platform, PhD Thesis, Business Computer Information System, University of Trier, Germany, 2008.
- ^ Harary, Frank; Norman, Robert Z.; Cartwright, Dorwin (1965), Structural Models: An Introduction to the Theory of Directed Graphs, New York: Wiley .
- ^ P. Resnik. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. Proc. the 14th International Joint Conference on Artificial Intelligence, 448–453, 1995.
- ^ WSM-P Workflow Semantic Matching Platform book: http://www.amazon.de/WSM-P-Workflow-Semantic-Matching-Platform/dp/3899638549/ref=sr_1_1?ie=UTF8&qid=1334607201&sr=8-1