Semantic similarity methods in wordNet and their application to information retrieval on the web

Authors:
Giannis Varelas;Epimenidis Voutsakis;Paraskevi Raftopoulou;Euripides G.M. Petrakis;Evangelos E. Milios
Affiliations:
Technical University of Crete (TUC), Crete, Greece;Technical University of Crete (TUC), Crete, Greece;Technical University of Crete (TUC), Crete, Greece;Technical University of Crete (TUC), Crete, Greece;Dalhousie University, Halifax, Nova Scotia, Canada
Venue:
Proceedings of the 7th annual ACM international workshop on Web information and data management
Year:
2005

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Abstract

Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.