Man Jianping, Shi Yufei, Hu Zhensheng, Yang Rui, Huang Zhisheng, Zhou Yi
Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080 China.
Knowledge Representation and Reasoning (KR &R) Group, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
Health Inf Sci Syst. 2024 Nov 14;12(1):54. doi: 10.1007/s13755-024-00309-3. eCollection 2024 Dec.
Kidney stone disease (KSD) is a common urological disorder with an increasing incidence worldwide. The extensive knowledge about KSD is dispersed across multiple databases, challenging the visualization and representation of its hierarchy and connections. This paper aims at constructing a disease-specific knowledge graph for KSD to enhance the effective utilization of knowledge by medical professionals and promote clinical research and discovery.
Text parsing and semantic analysis were conducted on literature related to KSD from PubMed, with concept annotation based on biomedical ontology being utilized to generate semantic data in RDF format. Moreover, public databases were integrated to construct a large-scale knowledge graph for KSD. Additionally, case studies were carried out to demonstrate the practical utility of the developed knowledge graph.
We proposed and implemented a Kidney Stone Disease Knowledge Graph (KSDKG), covering more than 90 million triples. This graph comprised semantic data extracted from 29,174 articles, integrating available data from UMLS, SNOMED CT, MeSH, DrugBank and Microbe-Disease Knowledge Graph. Through the application of three cases, we retrieved and discovered information on microbes, drugs and diseases associated with KSD. The results illustrated that the KSDKG can integrate diverse medical knowledge and provide new clinical insights for identifying the underlying mechanisms of KSD.
The KSDKG efficiently utilizes knowledge graph to reveal hidden knowledge associations, facilitating semantic search and response. As a blueprint for developing disease-specific knowledge graphs, it offers valuable contributions to medical research.
肾结石疾病（KSD）是一种常见的泌尿系统疾病，在全球范围内发病率呈上升趋势。关于KSD的广泛知识分散在多个数据库中，这对其层次结构和联系的可视化及呈现提出了挑战。本文旨在构建一个针对KSD的疾病特定知识图谱，以提高医学专业人员对知识的有效利用，并促进临床研究与发现。
对来自PubMed的与KSD相关的文献进行文本解析和语义分析，利用基于生物医学本体的概念注释生成RDF格式的语义数据。此外，整合公共数据库以构建一个大规模的KSD知识图谱。另外，进行案例研究以证明所开发知识图谱的实际效用。
我们提出并实现了一个肾结石疾病知识图谱（KSDKG），包含超过9000万个三元组。该图谱包含从29174篇文章中提取的语义数据，整合了来自UMLS、SNOMED CT、MeSH、DrugBank和微生物 - 疾病知识图谱的可用数据。通过三个案例的应用，我们检索并发现了与KSD相关的微生物、药物和疾病信息。结果表明，KSDKG可以整合多样的医学知识，并为识别KSD的潜在机制提供新的临床见解。
KSDKG有效地利用知识图谱揭示隐藏的知识关联，便于语义搜索和应答。作为开发疾病特定知识图谱的蓝图，它为医学研究做出了宝贵贡献。
