火灾应急领域知识图谱问答方法研究

Research on Q&A Method of Knowledge Graph in Fire Emergency Domain

火灾是常见的一种灾害,火灾应急知识的获取对个人安全和社会发展至关重要。文章提出一种面向火灾应急领域的知识图谱问答Pipeline(流水线模型)改进方法。首先对实体识别、实体链接及路径排序阶段的模型采用Task specific的思想进行独立训练,结合特征融合算法对知识三元组进行重新排序;其次在路径排序模型中引入Beam Search(集束搜索)算法;最后采用迁移学习策略,在通识领域的知识图谱问答语料场景下训练大参数量模型。实验证明,该方法应用于火灾应急领域语料上的准确率为89.0%,优于传统Pipeline方法,问答效果更好。

Fire is one of the most common disasters,and the acquisition of fire emergency knowledge is crucial for personal safety and social development.This paper proposes a method of improving knowledge graph Q&A(Questioning-answering)Pipeline(a pipeline model)for fire emergency domain.Firstly,the models for entity recognition,entity linking and path ranking are trained independently using the idea of Task specific,and the knowledge triplets is re-ranked by combining feature fusion algorithm.Secondly,Beam Search algorithm is introduced in the path ranking model.Finally,transfer learning strategy is adopted to train the large parametric model under the knowledge graph Q&A corpus of general domain.The experimental results show that the proposed method has an accuracy of 89.0%on the corpus of fire emergency field,which is better than the traditional Pipeline method,and has better Q&A performance.