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21-11-2016 | Methodologies and Application | Issue 6/2018

Soft Computing 6/2018

SISR: System for integrating semantic relatedness and similarity measures

Journal:
Soft Computing > Issue 6/2018
Authors:
Mohamed Ben Aouicha, Mohamed Ali Hadj Taieb, Abdelmajid Ben Hamadou
Important notes
Communicated by V. Loia.

Abstract

Semantic similarity and relatedness measures have increasingly become core elements in the recent research within the semantic technology community. Nowadays, the search for efficient meaning-centered applications that exploit computational semantics has become a necessity. Researchers, have therefore, become increasingly interested in the development of a model that can simulate the human thinking process and capable of measuring semantic similarity/relatedness between lexical terms, including concepts and words. Knowledge resources are fundamental to quantify semantic similarity or relatedness and achieve the best expression for the semantics content. No fully developed system that is able to centralize these approaches is currently available for the research and industrial communities. In this paper, we propose a System for Integrating Semantic Relatedness and similarity measures, SISR, which aims to provide a variety of tools for computing the semantic similarity and relatedness. This system is the first to treat the topic of computing semantic relatedness with a view of integrating different key stakeholders in a parameterized way. As an instance of the proposed architecture, we propose WNetSS which is a Java API allowing the use of a wide WordNet-based semantic similarity measures pertaining to different categories including taxonomic-based, features-based and IC-based measures. It is the first API that allows the extraction of the topological parameters from the WordNet “is a” taxonomy which are used to express the semantics of concepts. Moreover, an evaluation module is proposed to assess the reproducibility of the measures accuracy that can be evaluated according to 10 widely used benchmarks through the correlations coefficients.

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