Authors: Aditi Sharan, Manju Lata Joshi
Addresses: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi – 110067, India. ' School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi – 110067, India
Abstract: Semantic similarity is becoming a generic issue in variety of applications in areas of information retrieval, computational linguistic and AI, both in the academia and industry. Examples include: computing semantic similarity, word sense disambiguation, text segmentation, multimodal document retrieval, image retrieval, etc. However, semantic similarity measures have been used showing mixed chances of success. The basic problem is that if semantic measures are used bluntly without understanding, they might decrease retrieval efficiency. There is a need to investigate semantic similarity approaches in order to have better understanding of these approaches. Several semantic methods for determining semantic similarity between terms have been proposed in the literature and most of them have been tested on WordNet. In this paper, we investigate the approaches to compute semantic similarity by mapping word concepts to WordNet ontology and by examining their relationship in that ontology. The paper then provides specific examples for explaining these approaches Further, the paper categorises and compares various approaches for measuring semantic similarity using WordNet ontology.
Keywords: WordNet ontology; semantic similarity; similarity measures; information retrieval; computational linguistics; artificial intelligence.
International Journal of Information and Communication Technology, 2010 Vol.2 No.4, pp.331 - 341
Published online: 31 Aug 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article