[目的/意义]针对现有的中医领域知识图谱来源于科技文献的知识相对缺少的问题,提出一套面向中医科技文献实体关系抽取的解决方案,补充中医临床研究知识库,为领域知识图谱构建提供数据基础。[方法/过程]针对中医科技文献,设计领域实体关系表示模型;并根据领域数据多标签和重叠的特点,将实体关系抽取任务分解为关系分类和实体识别两个子任务,将关系分类结果融入实体识别任务中,设计基于预训练模型 BERT的实体关系抽取级联模型。[结果/结论]在自建中医科技文献信息抽取数据集(TCM-STD-IE)上进行实验验证,关系分类和实体识别的 F1-micro分别为 92. 74%、93. 58%。
[Purpose/Significance]Aiming at the relative lack of knowledge derived from scientific and technological document in the existing traditional Chinese medicine (TCM) knowledge graph,a set of solutions for entity relation extraction of TCM scientific and technological document is proposed to supplement the TCM clinical research knowledge base and provide a data foundation for the construction of domain knowledge graph.[Method/Process]Aiming at the scientific and technological document of TCM,a domain entity relationship representation model was designed.According to the characteristics of multi-label and overlapping domain data,the entity relationship extraction task was decomposed into two sub-tasks,relationship classification and entity recognition,and the relationship classification results were integrated into the entity recognition task.A cascaded model for entity relation extraction was dseigned based on the pre-trained model BERT.[Result/Conclusion]Experiments are carried out on the self-built TCM scientific and technological document information extraction data set (TCM-STD-IE),and the F1-micro of relation classification and entity recognition are 92.74% and 93.58%,respectively.
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