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SISR: System for integrating semantic relatedness and similarity measures

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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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Notes

  1. http://wnetss-api.smr-team.org/.

  2. http://wiki.dbpedia.org/About.

  3. Resource Description Framework (RDF) is a graph template to formally describe Web resources and their metadata so that such descriptions can be processed automatically.

  4. Information content-based approach quantifies the similarity between concepts as a function of the Information Content (IC) that both concepts have in common in a given ontology. The basic idea is that general and abstract entities found in a discourse present less IC than more concrete and specialized ones.

  5. Brown Corpus: The Brown University Standard Corpus of Present-Day American English (or just Brown Corpus) was compiled in the 1960s by Henry Kucera and W. Nelson Francis at Brown University, Providence, Rhode Island as a general corpus (text collection) in the field of corpus linguistics. It contains 500 samples of English-language text, with roughly one million words, compiled from works published in the USA in 1961.

  6. In simpler terms, a hyponym shares a type of relationship with its hypernym. For example, pigeon, crow, eagle and seagull are all hyponyms of bird (their hypernym); which, in turn, is a hyponym of animal.

  7. POS: Part Of Speech

  8. http://clic.cimec.unitn.it/~elia.bruni/MEN.html.

  9. http://wacky.sslmit.unibo.it/doku.php?id=corpora.

  10. http://www.cl.cam.ac.uk/~fh295/simlex.html.

  11. http://extjwnl.sourceforge.net/.

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Acknowledgements

The authors would like to express their sincere gratitude to Mr. Anouar Smaoui from the English Language Unit at the Faculty of Science of Sfax, Tunisia, for his valuable proofreading and language polishing services.

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Authors and Affiliations

  1. MIRACL Laboratory, Sfax University, Sfax, Tunisia

    Mohamed Ben Aouicha, Mohamed Ali Hadj Taieb & Abdelmajid Ben Hamadou

  2. Faculty of Sciences, Sfax University, Sfax, Tunisia

    Mohamed Ben Aouicha

  3. Higher Institute of Applied Sciences and Technology, Sousse University, Sousse, Tunisia

    Mohamed Ali Hadj Taieb

  4. Higher Institute of Computer Science and Multimedia, Sfax University, Sfax, Tunisia

    Abdelmajid Ben Hamadou

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  1. Mohamed Ben Aouicha
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  2. Mohamed Ali Hadj Taieb
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Correspondence to Mohamed Ali Hadj Taieb.

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Communicated by V. Loia.

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Ben Aouicha, M., Hadj Taieb, M.A. & Ben Hamadou, A. SISR: System for integrating semantic relatedness and similarity measures. Soft Comput 22, 1855–1879 (2018). https://doi.org/10.1007/s00500-016-2438-x

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