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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sana Ben Abdallah Ben Lamine & Mohamed Aziz Dachraoui & Hajer Baazaoui-Zghal, 2023. "Deep Learning-Based Extraction of Concepts: A Comparative Study and Application on Medical Data," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-28, August.
  • Handle: RePEc:wsi:jikmxx:v:22:y:2023:i:04:n:s0219649222500721
    DOI: 10.1142/S0219649222500721
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