Deep Learning-Based Extraction of Concepts: A Comparative Study and Application on Medical Data

Sana Ben Abdallah Ben Lamine (), Mohamed Aziz Dachraoui () and Hajer Baazaoui-Zghal ()
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Sana Ben Abdallah Ben Lamine: Riadi Laboratory, ENSI, University of Manouba, Tunisia
Mohamed Aziz Dachraoui: Riadi Laboratory, ENSI, University of Manouba, Tunisia
Hajer Baazaoui-Zghal: ETIS UMR8051, ENSEA, CY University CNRS F-95000, Cergy, France

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 04, 1-28

Abstract: With the exponential increase of data on the web, the manual acquisition of ontology has become a time-consuming and tedious task. Thus, switching to ontology learning enables the ontologies’ acquisition automation. In this paper, we deal with the phase of concepts’ extraction. Our motivation is to capitalise on the confirmed advantages of deep learning (DL) models and word embedding techniques to automatically extract relevant concepts from large amounts of textual data. A four phases approach is proposed where different models and techniques are applied and a comparative study is achieved: the preprocessing phase, the classification phase, based on DL models, the terms filtering phase, where we experimented and compared three methods to extract the relevant terms, and the semantic enrichment phase experimenting and comparing word embedding techniques to semantically enrich the discovered concepts. The approach is implemented and evaluated on different medical datasets. The obtained results proved the suitability of the experimented models and techniques for the concepts’ extraction.

Keywords: Ontology learning; concepts’ extraction; deep learning; word embedding techniques (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219649222500721

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