GPB and BAC:two novel models towards building an intelligent motor fault maintenance question answering system

Abstract

Generally, the existing methods for constructing a knowledge graph used in a question answering system adopted two different models respectively, one is for identifying entities, and the other is for extracting relationships between entities. However, this method may reduce the quality of knowledge because it is very difficult to keep contextual information consistent with the same entities in the two different models. To address this issue, this paper proposes a model called GPB (GlobalPointer + BiLSTM) which integrates the BiLSTM into GlobalPointer through concatenation operations to simultaneously guarantee the rationality of identified entities and relationships between entities. In addition, to enhance the user experience using an intelligent motor fault maintenance question answering system, a model called BAC (BiLSTM + Attention + CRF) is proposed to identify named entities in user questions, and the BERT-wwm model is used to classify user intentions to improve the quality of answers. Finally, to verify the advantages of the proposed model GPB and BAC, comparative experiments and real application effects of the developed question answering system are demonstrated on our built motor fault maintenance dataset. The experimental results indicate that the constructed knowledge graph and developed question answering system provide engineers with high-quality motor maintenance knowledge services.

Publication DOI: https://doi.org/10.1080/09544828.2024.2335135
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Funding Information: The authors wish to acknowledge the funding support from Shanghai Science and Technology Program under Grant 22010500900, the Mainland-Hong Kong Joint Funding Scheme of the Innovation and Technology Commission, Hong Kong Special Administration Region unde
Additional Information: Publisher Copyright: © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Uncontrolled Keywords: BiLSTM,GlobalPointer,Knowledge graph,motor fault maintenance,question answering system,Engineering(all)
Publication ISSN: 1466-1837
Last Modified: 18 Jun 2024 07:47
Date Deposited: 15 Apr 2024 14:01
Full Text Link:
Related URLs: https://www.tan ... 28.2024.2335135 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-04-12
Published Online Date: 2024-04-12
Accepted Date: 2024-03-22
Authors: Lyu, Pin
Fu, Jingqi
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Yu, Wenbing
Xia, Liqiao

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