Conditional Knowledge Extraction Using Contextual Information Enhancement
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Published:2023-04-14
Issue:8
Volume:13
Page:4954
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Xu Zhangbiao123, Zhang Botao123ORCID, Gu Jinguang123ORCID, Gao Feng123ORCID
Affiliation:
1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China 2. Institute of Big Data Science and Engineering, Wuhan University of Science and Technology, Wuhan 430065, China 3. Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content, National Press and Publication Administration, Beijing 100038, China
Abstract
Conditional phrases provide fine-grained domain knowledge in various industries, including medicine, manufacturing, and others. Most existing knowledge extraction research focuses on mining triplets with entities and relations and treats that triplet knowledge as plain facts without considering the conditional modality of such facts. We argue that such approaches are insufficient in building knowledge-based decision support systems in vertical domains, where specific and professional instructions on what facts apply under given circumstances are indispensable. To address this issue, this paper proposes a condition-aware knowledge extraction method using contextual information. In particular, this paper first fine-tunes the pre-training model to leverage a local context enhancement to capture the positional context of conditional phrases; then, a sentence-level context enhancement is used to integrate sentence semantics; finally, the correspondences between conditional phrases and relation triplets are extracted using syntactic attention. Experimental results on public and proprietary datasets show that our model can successfully retrieve conditional phrases with relevant triplets while improving the accuracy of the matching task by 2.68%, compared to the baseline.
Funder
National key research and development program National Natural Science Foundation of China Key Research and Development Program of Wuhan
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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