面向构建航空不安全事件知识图谱，开展知识图谱信息抽取技术研究。分析清洗航空不安全事件原始数据，构建航空不安全事件实体关系抽取数据集和领域词典。当前知识图谱中实体关系抽取的主要问题是实体边界获取困难和关系分类精度不高，鉴于此，提出融合领域词典嵌入的语义信息增强方法，进行领域实体关系联合抽取。以航空公司安全信息报告中的大量不安全事件记录为具体应用场景，对提出的知识图谱构建方法进行验证，实验分析提出的实体关系抽取算法的精确率和召回率指标，表明知识图谱构建方法的有效性。

For the purpose of constructing a knowledge graph for aviation unsafe events, information extraction technologies are focused on for knowledge graph. A dataset for entity relationship extraction and a domain dictionary have been constructed by analyzing and cleaning aviation unsafe events raw data. The major challenges in entity relationship extraction for knowledge graph are determining entity boundaries and improving relationship classification accuracy. In view of this, a semantic information augmentation method that integrated domain dictionary embedding was proposed for domain entity and relationship joint extraction. The proposed knowledge graph construction method was validated for the scenario of numerous unsafe event recordings in airline safety information reports. Experiments demonstrate the effectiveness of the knowledge graph construction method by analyzing the precision and recall metrics of the proposed entity relationship extraction algorithms.