【摘要】 面向医学知识的细粒度、可共享性与高精准性的需求，该文提出了中文医学文本知识表示体系，融合了电子病历、医学书籍与专业医学网站文本三个数据来源的医疗知识。该体系包括9类医学实体、60类实体关系。基于此，开发了可操作性高的标注工具，并为每种来源提供了规范标注的医学文本数据，构建了涵盖范围广、一致性高的细粒度标注语料库。4名临床医生对《诊断学》书籍标注了6 526个医学实体，4 229条关系，标注一致性可达0.974。三个数据源融合后实体数量344 475个，关系数量3 196 787条。该文综述了数据源融合的映射过程、标注细则，分析了各数据源的文本特点并总结标注模式，通过应用场景与文本特点表明医学书籍标注必要性。该文为中文医学语料库构建提供标注规范，并为中文医学实体识别与关系抽取提供语料支持。更多还原

【Abstract】 To build a fine-grained, sharable, and high-quality knowledge base in the medical field, we propose a Chinese medical knowledge representation system to cover Chinese clinical texts including electronic medical records, books, and professional medical web text data. This system defines 9 entity types and 60 entity relation types. Then we develop a highly operable annotation tool and construct a public available annotated corpus with wide coverage and high consistency for all three text sources. Four annotators annotate the Chinese medical book named “Diagnostics” with 0.974 inter-annotator agreement, generating altogether 6 526 medical entities and 4 229 entity relations. The whole corpus consists of 344 475 medical entities and 3 196 787 entity relations without duplications. The paper presents the mapping scheme, annotation rules for knowledge fusion, as well as the text characteristics of each data source. As a pioneering work for Chinese corpus of medical entity recognition and relation extraction, this paper provides an annotation standard for Chinese medical construction.更多还原