Construction of MeSH-Like Obstetric Knowledge Graph

Abstract

Obstetric Knowledge Graph describes the obstetric diseases and the body parts, drugs, etc. as well as the relations between them, which is an important knowledge base for intelligent auxiliary diagnosis. In this paper, the hierarchical structure of Medical Subject Headings(MeSH) is taken as the ontology prototype of the Knowledge Graph. According to the naming criterion of Chinese obstetric diseases and the practical application of the disease diagnosis in obstetric electronic medical records. And the ontology structure of obstetric diseases is extended. Moreover, the possible relation categories between obstetric entities and knowledge description system are defined to form the schema layer of Obstetric Knowledge Graph. The obstetric disease attributes of heterogeneous data are derived from medical specifications, classic textbooks, and medical online website by using rules-based and wrapper methods. And then they are fused by the Simhash-TF-IDF algorithm. The relations between entities in the knowledge graph are extracted by combining Bootstrapping and SVM algorithms. Then the Obstetric Knowledge Graph data layer is completed. The schema layer and data layer are automatically imported into Protege to visualize the Obstetric Knowledge graph. The constructed Obstetric Knowledge Graph contains 625 entities, 2456 attributes and 1407 relations, which covers the most diseases in obstetrics and related entities.