Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data

Ahmed, Rafeeq and Singh, Pradeep and Ahmad, Tanvir (2020) Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data. EAI Endorsed Transactions on Energy Web, 8 (31). e5. ISSN 2032-944X

[img]
Preview
Text
eai.13-7-2018.165503.pdf
Available under License Creative Commons Attribution No Derivatives.

Download (1MB) | Preview

Abstract

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

Item Type: Article
Uncontrolled Keywords: Text Mining, Semantic Similarity, Concept Extraction
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 07 Apr 2021 07:22
Last Modified: 07 Apr 2021 07:22
URI: https://eprints.eudl.eu/id/eprint/2244

Actions (login required)

View Item View Item