Knowledge graphs (KGs) serve as powerful tools for organizing and
representing structured knowledge. While their utility is widely recognized,
challenges persist in their automation and completeness. Despite efforts in
automation and the utilization of expert-created ontologies, gaps in
connectivity remain prevalent within KGs. In response to these challenges, we
propose an innovative approach termed ``Medical Knowledge Graph Automation
(M-KGA)". M-KGA leverages user-provided medical concepts and enriches them
semantically using BioPortal ontologies, thereby enhancing the completeness of
knowledge graphs through the integration of pre-trained embeddings. Our
approach introduces two distinct methodologies for uncovering hidden
connections within the knowledge graph: a cluster-based approach and a
node-based approach. Through rigorous testing involving 100 frequently
occurring medical concepts in Electronic Health Records (EHRs), our M-KGA
framework demonstrates promising results, indicating its potential to address
the limitations of existing knowledge graph automation techniques.

医疗知识图谱自动化 (M-KGA) 是一种创新方法，通过利用用户提供的医学概念并利用 BioPortal 本体进行语义增强，以整合预训练嵌入向量，从而提高知识图谱的完整性。该方法引入了基于聚类和基于节点的两种方法，以发现知识图谱中隐藏的连接，通过对电子健康记录中 100 个频繁出现的医学概念进行严格测试，展示了有望解决现有知识图谱自动化技术限制的潜力。