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Automatic ontology construction from text: a review from shallow to deep learning trend

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Abstract

The explosive growth of textual data on the web coupled with the increase on demand for ontologies to promote the semantic web, have made the automatic ontology construction from the text a very promising research area. Ontology learning (OL) from text is a process that aims to (semi-) automatically extract and represent the knowledge from text in machine-readable form. Ontology is considered one of the main cornerstones of representing the knowledge in a more meaningful way on the semantic web. Usage of ontologies has proven to be beneficial and efficient in different applications (e.g. information retrieval, information extraction, and question answering). Nevertheless, manually construction of ontologies is time-consuming as well extremely laborious and costly process. In recent years, many approaches and systems that try to automate the construction of ontologies have been developed. This paper reviews various approaches, systems, and challenges of automatic ontology construction from the text. In addition, it also discusses ways the ontology construction process could be enhanced in the future by presenting techniques from shallow learning to deep learning (DL).

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Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia

    Fatima N. Al-Aswadi, Huah Yong Chan & Keng Hoon Gan

  2. Faculty of Computer Sciences and Engineering, Hodeidah University, Hodeidah, Yemen

    Fatima N. Al-Aswadi

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  1. Fatima N. Al-Aswadi
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Al-Aswadi, F.N., Chan, H.Y. & Gan, K.H. Automatic ontology construction from text: a review from shallow to deep learning trend. Artif Intell Rev 53, 3901–3928 (2020). https://doi.org/10.1007/s10462-019-09782-9

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