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Semi-automated ontology development scheme via text mining of scientific records

Somayeh Tamjid (Department of Communication and Knowledge Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Fatemeh Nooshinfard (Department of Communication and Knowledge Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Molouk Sadat Hosseini Beheshti (Department of Information Science Research, Iranian Research Institute for Information Science and Technology, Tehran, Iran)
Nadjla Hariri (Department of Communication and Knowledge Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Fahimeh Babalhavaeji (Department of Communication and Knowledge Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran)

The Electronic Library

ISSN: 0264-0473

Article publication date: 6 February 2024

Issue publication date: 10 April 2024

95

Abstract

Purpose

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

Design/methodology/approach

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

Findings

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

Originality/value

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

Keywords

Citation

Tamjid, S., Nooshinfard, F., Hosseini Beheshti, M.S., Hariri, N. and Babalhavaeji, F. (2024), "Semi-automated ontology development scheme via text mining of scientific records", The Electronic Library, Vol. 42 No. 2, pp. 230-254. https://doi.org/10.1108/EL-06-2023-0165

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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