Title: A comprehensive survey of text-based semantic similarity with potential applications

Authors: Puneet Sharma; K. Yogeswara Rao; P. Kavitha; T. Sakthivel

Addresses: Unitedworld Institute of Technology, Karnavati University, Gandhinagar, Gujarat, India ' Department of Computer Science and Engineering, GITAM School of Technology, GITAM, Rushikonda, Visakhapatnam, India ' Department of CSE, PES University, Bengaluru, Karnataka, India ' Firstsoft Technologies Pvt. Ltd., Chennai, India

Abstract: Text-based semantic similarity has recently become a popular research topic, and estimating the semantic similarity between entities for learning and decision-making is a challenging research problem. Semantic similarity quantitatively measures the informativeness between the data objects based on their properties and relationships. Due to the vast availability of measures developed by domain experts from different research fields, choosing an appropriate semantic similarity measure that suits a specific application context is challenging. The primary objective of this systematic literature survey is to offer a comprehensive view of semantic similarity and potential applications that emphasise the enormous diversity of research contributions in a wide range of fields. In this context, it examines various categories of semantic similarity based on the underlying principles of the state-of-the-art approaches with pros and cons. Notably, the prospective application areas are explored extensively by highlighting their significance. Finally, the survey concludes by summarising valuable future research directions.

Keywords: semantic similarity; natural language processing; NLP; corpus-based; knowledge-based; semantic similarity applications; deep learning; DL.

DOI: 10.1504/IJIIDS.2025.145456

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.2, pp.143 - 185

Received: 16 Feb 2024
Accepted: 15 Jun 2024
Published online: 01 Apr 2025 *

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