Title: Towards a hybrid semantic similarity measure to set the conceptual relatedness in a hierarchy

Authors: Djamel Nessah; Okba Kazar; Aïcha-Nabila Benharkat

Addresses: ICOSI Laboratory, Abbès Laghrour University, BP: 1252, Route de Constantine, Khenchela 40004, Algeria ' LINFI Laboratory, Mohamed Khider University, Biskra 07000, Algeria ' LIRIS Laboratory, INSA, 7 av. Jean Capelle, Villeeurbanne, Lyon 69621, France

Abstract: Assessment of semantic similarity between concepts is of great importance in many applications dealing with textual data, such as natural language processing, knowledge acquisition, document semantic annotation and information retrieval systems. Moreover, to extract similar concepts from multiple ontologies, there is a real need to develop a conceptual similarity measure, the intention of finding semantic similarity in a given hierarchy, is to enhance the integration and retrieval of heterogeneous information resources in a more meaningful and accurate way. In this paper we present a hybrid approach for measuring the semantic similarity, in an attempt to address a new issue that focuses on the sensitivity of the similarity measure between concepts in a hierarchy. Based on the notions of both distance and the information content, the measure is expected to provide more consistent and accurate measures.

Keywords: semantic similarity; semantic relatedness; edge counting; information content; hybrid similarity; WordNet taxonomy; ontology; conceptual relations; Wu-Palmer measure; Lin measure; similarity measures; hierarchy.

DOI: 10.1504/IJMSO.2016.081583

International Journal of Metadata, Semantics and Ontologies, 2016 Vol.11 No.3, pp.155 - 164

Received: 24 May 2016
Accepted: 03 Oct 2016
Published online: 16 Jan 2017 *

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