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Implementing WordNet Measures of Lexical Semantic Similarity in a Fuzzy Logic Programming System

Published online by Cambridge University Press:  03 March 2021

PASCUAL JULIÁN-IRANZO Open the ORCID record for PASCUAL JULIÁN-IRANZO [Opens in a new window]  and
FERNANDO SÁENZ-PÉREZ Open the ORCID record for FERNANDO SÁENZ-PÉREZ [Opens in a new window]
PASCUAL JULIÁN-IRANZO
Affiliation:
Department of Information Technologies and Systems, University of Castilla-La Mancha, 13071Ciudad Real, Spain (e-mail: Pascual.Julian@uclm.es)
FERNANDO SÁENZ-PÉREZ
Affiliation:
Faculty of Computer Science, Complutense University of Madrid, 28040Madrid, Spain (e-mail: fernan@sip.ucm.es)
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Abstract

This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be generated with an approximation degree. Proximity equations are the key syntactic structures which, in addition to a weak unification algorithm, make a flexible query-answering process possible in this kind of programming language. This addition widens the scope of Fuzzy Logic Programming, allowing certain forms of lexical reasoning, and reinforcing Natural Language Processing (NLP) applications.

Keywords

fuzzy logic programmingWordNetproximity equationssystem implementation
Type
Technical Note
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 2 , March 2021 , pp. 264 - 282
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

*

Work is partially funded by the State Research Agency (AEI) of the Spanish Ministry of Science and Innovation under grant PID2019-104735RB-C42 (SAFER), by the Spanish Ministry of Economy and Competitiveness, under the grants TIN2016-76843-C4-2-R (MERINET), TIN2017-86217-R (CAVI-ART-2), and by the Comunidad de Madrid, under the grant S2018/TCS-4339 (BLOQUES-CM), co-funded by EIE Funds of the European Union.

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