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Weighting-based semantic similarity measure based on topological parameters in semantic taxonomy

Published online by Cambridge University Press:  04 June 2018

ABDULGABBAR SAIF Open the ORCID record for ABDULGABBAR SAIF [Opens in a new window] ,
UMMI ZAKIAH ZAINODIN ,
NAZLIA OMAR  and
ABDULLAH SAEED GHAREB
ABDULGABBAR SAIF
Affiliation:
Department of Computer Information Systems, Faculty of IT&CS, University of Saba Region, Marib, Yemen e-mail: agmssaif@gmail.com, aghurieb@usr.ac
UMMI ZAKIAH ZAINODIN
Affiliation:
Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Malaysia e-mail: ummizakiahzainodin@gmail.com, nazlia@ukm.edu.my
NAZLIA OMAR
Affiliation:
Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Malaysia e-mail: ummizakiahzainodin@gmail.com, nazlia@ukm.edu.my
ABDULLAH SAEED GHAREB
Affiliation:
Department of Computer Information Systems, Faculty of IT&CS, University of Saba Region, Marib, Yemen e-mail: agmssaif@gmail.com, aghurieb@usr.ac
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Abstract

Semantic measures are used in handling different issues in several research areas, such as artificial intelligence, natural language processing, knowledge engineering, bioinformatics, and information retrieval. Hierarchical feature-based semantic measures have been proposed to estimate the semantic similarity between two concepts/words depending on the features extracted from a semantic taxonomy (hierarchy) of a given lexical source. The central issue in these measures is the constant weighting assumption that all elements in the semantic representation of the concept possess the same relevance. In this paper, a new weighting-based semantic similarity measure is proposed to address the issues in hierarchical feature-based measures. Four mechanisms are introduced to weigh the degree of relevance of features in the semantic representation of a concept by using topological parameters (edge, depth, descendants, and density) in a semantic taxonomy. With the semantic taxonomy of WordNet, the proposed semantic measure is evaluated for word semantic similarity in four gold-standard datasets. Experimental results show that the proposed measure outperforms hierarchical feature-based semantic measures in all the datasets. Comparison results also imply that the proposed measure is more effective than information-content measures in measuring semantic similarity.

Type
Article
Information
Natural Language Engineering , Volume 24 , Issue 6 , November 2018 , pp. 861 - 886
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

*

This work was partially funded by the Ministry of Higher Education in Malaysia under the grant no. (FRGS/1/2016/ICT02/UKM/02/11). The first author would like to thank the University of Saba Region for its financial supports.

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