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Web-Based Measure of Semantic Relatedness

Identifieur interne : 001310 ( Istex/Corpus ); précédent : 001309; suivant : 001311

Web-Based Measure of Semantic Relatedness

Auteurs : Jorge Gracia ; Eduardo Mena

Source :

RBID : ISTEX:C113A7C30566470503BEE2E028F94C08AC3054F9

Abstract

Abstract: Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality. In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching.

Url:
DOI: 10.1007/978-3-540-85481-4_12

Links to Exploration step

ISTEX:C113A7C30566470503BEE2E028F94C08AC3054F9

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<Para>Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality.</Para>
<Para>In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching.</Para>
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