Intelligent question answering knowledge subgraph matching of high-speed train component and parts knowledge graph
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摘要: 为应对高速列车零部件知识复杂、海量且多层级的特点，提高高速列车零部件知识图谱智能问答的效果，提出了一种基于情景感知和分类模型的高速列车零部件知识图谱智能问答知识子图匹配模型。该模型通过情景模型进行情景特征提取及向量转换；再将词向量和情景向量相融合，输入到BERT（Bidirectional Encoder Representation from Transformers）模型中，进行用户问句的所属知识域分类，分类结果即为知识子图匹配的结果。经试验证明，所提模型与其他主流分类模型相比，各项性能指标更优。Abstract: In order to address the complexity, magnanimity, and multi-level characteristics of high-speed train components and parts knowledge, and improve the effectiveness of intelligent Q&A for high-speed train components and parts knowledge graph, this paper proposed a knowledge subgraph matching model of high-speed train components and parts knowledge graph based on situational awareness model and classification model. This model extracted scene features and transformed vectors through situational model, fused the word vector and situational vector, input them into the Bidirectional Encoder Representation from Transformers (BERT) model, and classified the knowledge domain to which the user question belongs. The classification results were the results of knowledge subgraph matching. Experimental results show that the proposed model has better performance indicators in all aspects compared with other mainstream classification models.
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表 3 4种模型实验结果对比
模型 宏精确率 宏召回率 宏F1值 Kg-BERT 0.97 0.97 0.96 K-BERT 0.94 0.93 0.93 BERT 0.93 0.91 0.92 BERT+情景向量 0.98 0.98 0.96 
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