Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data

Abstract

Semantic Relatedness computation has been a fundamental as well as an essential step for domains likeInformation Retrieval, Natural Language Processing, Semantic Web, etc. Many techniques for SemanticRelatedness calculation in a single domain have been proposed. However, these techniques give inappropriateresults for the massive multidomain dataset because they provide a relation between concepts across differentdomains, which are not related to each other. Their similarities should be minimized. In this paper, anovel method, "modified Balanced Mutual Information(MBMI)," to calculate the semantic relatedness ofmultidomain data has been proposed. In this proposed method, to get semantic relatedness, concepts areextracted, followed by a fuzzy vector from a given corpus. A comparison of the proposed method with otherexisting methods has been performed. We used medical and computer science articles as our dataset. Theproposed method shows better results for multidomain data.

Keywords

• text mining
• semantic similarity
• concept extraction