Sun Yueping, Li Jiao, Xu Zidu, Liu Yan, Hou Li, Huang Zhisheng
Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, 100020 China.
Knowledge Representation and Reasoning (KR\&R) Group, Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Health Inf Sci Syst. 2022 Sep 10;10(1):27. doi: 10.1007/s13755-022-00179-7. eCollection 2022 Dec.
Researchers have identified gut microbiota that interact with brain regions associated with emotion and mood. Literature reviews of those associations rely on rigorous systematic approaches and labor-intensive investments. Here we explore how knowledge graph, a large scale semantic network consisting of entities and concepts as well as the semantic relationships among them, is incorporated into the emotion-probiotic relationship exploration work.
We propose an end-to-end emotion-probiotics relationship exploration method with an integrated medical knowledge graph, which incorporates the text mining output of knowledge graph, concept reasoning and evidence classification. Specifically, a knowledge graph for probiotics is built based on a text-mining analysis of PubMed, and further used to retrieve triples of relationships with reasoning logistics. Then specific relationships are annotated and evidence levels are retrieved to form a new evidence-based emotion-probiotic knowledge graph.
Based on the probiotics knowledge graph with 40,442,404 triples, totally 1453 PubMed articles were annotated in both the title level and abstract level, and the evidence levels were incorporated to the visualization of the explored emotion-probiotic relationships. Finally, we got 4131 evidenced emotion-probiotic associations.
The evidence-based emotion-probiotic knowledge graph construction work demonstrates an effective reasoning based pipeline of relationship exploration. The annotated relationship associations are supposed be used to help researchers generate scientific hypotheses or create their own semantic graphs for their research interests.
研究人员已确定肠道微生物群与大脑中与情绪和心境相关的区域存在相互作用。对这些关联的文献综述依赖于严格的系统方法和大量人力投入。在此，我们探讨如何将知识图谱（一种由实体和概念以及它们之间的语义关系组成的大规模语义网络）纳入情绪与益生菌关系的探索工作中。
我们提出一种端到端的情绪与益生菌关系探索方法，该方法集成了医学知识图谱，其中纳入了知识图谱的文本挖掘输出、概念推理和证据分类。具体而言，基于对PubMed的文本挖掘分析构建了一个益生菌知识图谱，并进一步用于检索具有推理逻辑的关系三元组。然后对特定关系进行注释，并检索证据水平，以形成一个新的基于证据的情绪与益生菌知识图谱。
基于包含40442404个三元组的益生菌知识图谱，共有1453篇PubMed文章在标题级别和摘要级别进行了注释，并且证据水平被纳入到所探索的情绪与益生菌关系的可视化中。最后，我们得到了4131个有证据支持的情绪与益生菌关联。
基于证据的情绪与益生菌知识图谱构建工作展示了一种有效的基于推理的关系探索流程。所注释的关系关联应用于帮助研究人员生成科学假设或为其研究兴趣创建自己的语义图谱。
