Abstract: Based on a path reasoning method of graph encoder, we used the entity relationships between multi rounds of dialogue in the knowledge graph as a node graph. The encoder sequentially encoded the nodes according to each round of dialogue to simulate the semantic reasoning process, and utimately predicted the answer entity for the current dialogue. This approach solved the problems of missing words and pronouns in dialogues, as well as feature extraction problems in complex contexts. The experimental results show that the method focused more on the relationships between entities, which helped to maintain the integrity and accuracy of reasoning. To a certain extent, it proved the practicality and effectiveness of modeling context as a relational node graph.

Key words:  , knowledge graph, natural language process, multi round of question answering, convolutional neural network