Clustered Hierarchical Concept Based Semantic Closeness Between Two Concepts Using WordNet

• Published in: International journal of computer science issues Vol. 11; no. 4; p. 33
• Main Authors: Rao, Boddu Bhaskara, Kumari, Vatsavayi Valli
• Format: Journal Article
• Language: English
• Published: Mahebourg International Journal of Computer Science Issues (IJCSI) 01.07.2014
• Subjects: Clustering; Computer science; Fuzzy; Information retrieval; Mathematical analysis; Networks; Search engines; Semantics; Texts
• ISSN: 1694-0814; 1694-0784

The search engine needs relatedness to measure closeness between two concepts for determining optimal results in major applications like information retrieval, information integration and of many more in natural language processing tasks ie text classification, word sense disambiguation, matching problems in artificial intelligence etc. The clustered hierarchical concept network helps to overcome the fuzzy variations in different levels of granularity in measures of closeness based on weights, frequency or distances but these measures are not considered since no method takes the actual context of the user intention, user query or context domain subject fields. In this article, the authors have proposed a method for computing semantic closeness of two concepts in which the holonyms, meronyms, instances of concepts are considered synthetically. By calculating test data, the experiment results show that, the method can compute concepts closeness effectively. The human judgments on a set of concept pairs led their approach to be more effective and have shown one of the best performance than the measures based on concept vector.