【摘要】 [目的/意义]从数据基础层、关键技术层、领域知识图谱构建层及场景式应用层4个层次构建面向辅助决策的领域知识图谱及其场景式应用框架，为知识发现提供系统性支撑。[方法/过程]在构建面向辅助决策的领域知识图谱及其场景式应用框架的基础上，针对篇章级文本细粒度实体及其关系抽取任务开展模型改进及性能评估，选取最优模型构建领域知识图谱，以变压器设备故障领域为例开展实证，实现面向辅助决策的场景式应用。[结果/结论]融合改进的BERT-BiLSTM-CRF模型与PURE-RE模型的Pipeline知识抽取方法，综合性能较为优秀，构建的变压器故障领域知识图谱的检索结果通过了基于《设备标准缺陷知识库》的专家知识验证，能够有效辅助运维人员进行设备检修与维护，验证了本研究所构建框架的有效性及实用性，在领域专家知识验证的支撑下可有效赋能相关领域开展辅助决策。更多还原

【Abstract】 [Purpose/significance]This research constructed a domain knowledge graph and its scenario-oriented application framework for decision support at four levels: the data foundation layer, the key technology layer, the domain knowledge graph construction layer, and the scenario-oriented application layer. This framework aims to provide systematic support for knowledge discovery. [Method/process]Based on the construction of a domain knowledge graph and its scenario-oriented application framework for decision support, this research focuses on the improvement of models and performance evaluation for fine-grained entity and relationship extraction at the discourse level within texts. The optimal model is selected to construct a domain knowledge graph. Taking the field of transformer equipment failure as an example, empirical studies are conducted to realize scenario-oriented applications aimed at decision support. [Result/conclusion]The integrated pipeline knowledge extraction method, which combines the improved BERT-BiLSTMCRF model and the PURE-RE model, has demonstrated superior comprehensive performance. The knowledge graph constructed for the domain of transformer faults has been verified against the expert knowledge in the "Equipment Standard Defect Knowledge Base", and the search results have passed verification. This effectively assists maintenance personnel in equipment inspection and maintenance, thereby validating the effectiveness and practicality of the framework established in this research. With the support of domain expert knowledge verification, it can effectively empower related fields to conduct decision support.更多还原