An enhanced ontology based measure of similarity between words and semantic similarity search

Abstract

Measures of Semantic Similarity of two sets of words that describe two entities is an important problem in Web Mining. Semantic Similarity measures are used in various applications in Information Retrieval (IR) , Natural Language Processing (NLP) such as Word Sense Disambiguation (WSD), synonym extraction, query expansion and automatic thesauri extraction. The Computer being a syntactic machine, it cannot understand the semantics. Ontology is the explicit specialization of concepts, attributes and the relationships between them. It is for providing relevant and accurate information to the users for a particular domain. A new Semantic Similarity measure based on the domain Ontology is proposed here. It brings out a more accurate relationship between the two words The main purpose of finding Semantic Similarity is to enhance the integration and retrieval of resources in a more meaningful and accurate way. The performance analysis in terms of Precision and Recall for Traditional Search and Semantic Similarity Search is done. The Precision value of Semantic Similarity Search is high compared with the Traditional Search. This paper focuses on the approaches that differentiates the Semantic Similarity Research from other related areas.