A review for ontology construction from unstructured texts by using deep learning

Shandong Yuan; Jun He; Min Wang; Han Zhou; Yun Ren

doi:10.1117/12.2628713

22 April 2022 A review for ontology construction from unstructured texts by using deep learning

Shandong Yuan, Jun He, Min Wang, Han Zhou, Yun Ren

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121741D (2022) https://doi.org/10.1117/12.2628713
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Ontology is effectively formal, clear and detailed specifications in the form of concepts and relations of a shared conceptualization to a special domain. Ontology construction methods can be classified into manual construction and (semi-)automatic construction. However, manual construction method is usually expensive due to the considerable amount of human efforts it may involve. Therefore, automatic and semi-automatic ontology construction has been a research hotspot in the past decade. A new trend of these approaches is relying on machine learning and automatic language processing technology to extract concepts and ontology relationships from structured or unstructured data (such as database and text). The aim of this paper is to introduce some recent representative technical researches on ontology construction using deep learning model from text.

Citation Download Citation

Shandong Yuan, Jun He, Min Wang, Han Zhou, and Yun Ren "A review for ontology construction from unstructured texts by using deep learning", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121741D (22 April 2022); https://doi.org/10.1117/12.2628713

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