Research Article
Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data
@ARTICLE{10.4108/eai.13-7-2018.165503, author={Rafeeq Ahmed and Pradeep Kumar Singh and Tanvir Ahmad}, title={Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data}, journal={EAI Endorsed Transactions on Energy Web}, volume={8}, number={31}, publisher={EAI}, journal_a={EW}, year={2020}, month={6}, keywords={Text Mining, Semantic Similarity, Concept Extraction}, doi={10.4108/eai.13-7-2018.165503} }- Rafeeq Ahmed
Pradeep Kumar Singh
Tanvir Ahmad
Year: 2020
Novel Semantic Relatedness Computation for Multi-Domain Unstructured Data
EW
EAI
DOI: 10.4108/eai.13-7-2018.165503
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.
Copyright © 2020 Rafeeq Ahmed et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
