Adapting Gloss Vector semantic relatedness measure for semantic similarity estimation: An evaluation in the biomedical domain

Pesaranghader, Ahmad and Rezaei, Azadeh and Pesaranghader, Ali (2014) Adapting Gloss Vector semantic relatedness measure for semantic similarity estimation: An evaluation in the biomedical domain. In: Semantic Technology. Lecture Notes in Computer Science . Springer International Publishing, pp. 129-145. ISBN 978-3-319-06825-1

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Abstract

Automatic methods of ontology alignment are essential for establishing interoperability across web services. These methods are needed to measure semantic similarity between two ontologies’ entities to discover reliable correspondences. While existing similarity measures suffer from some difficulties, semantic relatedness measures tend to yield better results; even though they are not completely appropriate for the ‘equivalence’ relationship (e.g. “blood” and “bleeding” related but not similar). We attempt to adapt Gloss Vector relatedness measure for similarity estimation. Generally, Gloss Vector uses angles between entities’ gloss vectors for relatedness calculation. After employing Pearson’s chi-squared test for statistical elimination of insignificant features to optimize entities’ gloss vectors, by considering concepts’ taxonomy, we enrich them for better similarity measurement. Discussed measures get evaluated in the biomedical domain using MeSH, MEDLINE and dataset of 301 concept pairs. We conclude Adapted Gloss Vector similarity results are more correlated with human judgment of similarity compared to other measures.

Item Type: Book Section
Additional Information: Book Subtitle: Third Joint International Conference, JIST 2013, Seoul, South Korea, November 28--30, 2013, Revised Selected Papers
Subjects: T Technology > T Technology (General)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 21 Jul 2014 08:42
Last Modified: 21 Jul 2014 08:42
URI: http://shdl.mmu.edu.my/id/eprint/5634

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