利用知识图谱发现营养失调与肠道微生物群的关系

Jiahui Hu, Zhisheng Huang, Wei Chen, Pei Lou, Wanqing Zhao, Kuanda Yao, An Fang
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引用次数: 0

摘要

营养与公众健康密切相关,越来越多的研究表明,肠道微生物群与营养失调之间存在许多相关性。这些研究构成了医学知识的基本来源。然而,从海量数据中发现知识,单靠人力是不容易的。本研究旨在利用知识图谱从生物医学文献中发现营养失调与肠道微生物群之间的关系。本研究利用SPARQL查询和逻辑推理技术,从海量数据中自动即时提取文献内容信息。然后,获得一个注释语料库,其中包括营养失调和肠道微生物群之间关系的分类和定义。从而构建了营养失调与肠道微生物群语义关系的知识图谱,并发现了语义关系,验证了利用知识图谱发现关系的可行性和高效性。此外,对证据案例的分析表明,利用证据的重要性排除矛盾结论是必要的和有价值的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Relationships Discovery between Nutritional Disorders and Gut Microbiota with Knowledge Graphs
Nutrition is closely associated with public health, and more and more studies have demonstrated that there are many correlations between the gut microbiota and nutritional disorders. These studies constitute the basic source of medical knowledge. However, discovering knowledge from massive amounts of data is not easy only by manpower. This study aims at discovering the relationships between nutritional disorders and gut microbiota from the biomedical literatures with knowledge graph. In this study, literature content information from massive data is automatically and instantly extracted using SPARQL query and logic reasoning technologies. Then, an annotated corpus is obtained with the categorization and definition of relationships between nutritional disorders and gut microbiota. Thus, a knowledge graph of semantic relationship between nutritional disorders and gut microbiota is constructed, and semantic relationships are discovered, verifying the feasibility and high efficiency of relationships discovery with knowledge graphs. Moreover, the analysis of evidence case shows that it is necessary and valuable to use the importance of evidence to exclude conflicting conclusions.
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