Construction and evaluation of a domain-specific knowledge graph for knowledge discovery
Construction and evaluation of a domain-specific knowledge graph for knowledge discovery
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DOI：
10.1108/idd-06-2022-0054
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发表时间：
2023-02-03
影响因子：
2.1
通讯作者：
Ding, Junhua
中科院分区：
文献类型：
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作者：
Nguyen, Huyen;Chen, Haihua;Ding, Junhua
目的本研究旨在评估构建生物医学知识图（KG）的方法。设计/方法/途径本研究首先在COVID-19开放研究数据集上构建了一个COVID-19知识图谱，涵盖六大类信息（即疾病、药物、基因、物种、治疗和症状）。该构建使用开源工具来提取实体、关系和三元组。然后，使用半自动方法从三个数据质量维度评估 COVID-19 KG：正确性、相关性和全面性。最后，本研究通过构建问答系统来评估知识图谱的应用。有关 COVID-19 基因组、症状、传播和治疗的五个查询已提交给系统，并对结果进行了分析。研究发现，目前的抽取工具，知识图谱的质量一般，很难提高，除非在实体抽取、关系抽取等工具上做出更多的努力来改进。这项研究发现，全面性和相关性与数据大小呈正相关。此外，结果表明，对于大多数查询，基于大规模知识图谱构建的问答系统的性能优于小型知识图谱系统，证明了相关性和全面性对于确保知识图谱有用性的重要性。原创性/价值 本文讨论的知识图谱构建过程、基于数据质量和基于应用的评估为知识图谱研究人员和实践者构建高质量的特定领域知识发现系统提供了有价值的参考。
PurposeThis study aims to evaluate a method of building a biomedical knowledge graph (KG). Design/methodology/approachThis research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed. FindingsWith current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG. Originality/valueThe KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.