关键词: 认知图谱,  , 问答系统,  , GCN 模型,  , 推理模型

Abstract: At present, most of the existing question answering system models use template matching for reasoning, which is not enough for question reasoning. Therefore, a question answering system reasoning model is proposed based on cognitive map. Firstly, the ontology is constructed based on the domain knowledge as the knowledge source, and then the question relation one-to-one cognitive map question answering system model is constructed based on the cognitive map. Finally, the question and answer is divided into simple question and complex question, and the simple question is matched by BERT + CRF(Bidirectional Encoder Representations from Transformers+Conditional Random Field) model. For the complex question, node2vec is used to generate subgraph, then GCN(Graph Convolutional Network) reasoning model is used for reasoning, and the answer is taken as the output result. The proposed model is tested in the field of underground operation, and the results show that the cognitive map Question answering model is better than other algorithm models.

Key words: cognitive map, question answering system, graph convolutional network ( GCN ) model, reasoning model