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| This paper designs a dialogue model based on the granulated knowledge graph (Granulation Knowledge aware Dialogue Model, GKDM). Through the method of structural granulation, the knowledge graph is divided into two modules: local knowledge graph and non-local knowledge graph, respectively capturing the local semantics adjacent to the knowledge entity and the overall semantic representation of the located knowledge sub-graph, which solves the problem of the division of the knowledge graph. This paper designs the static graph attention mechanism and the hierarchical dynamic graph attention mechanism to participate in the process of encoding and decoding in the model respectively, and aggregates the semantics of knowledge entities through multi-level attention weights, and fuses them into the text vector, which solves the problem of the fusion of multi-layer knowledge information. Based on the above two innovations, the model proposed in this paper alleviates the problems of the current knowledge graph-based dialogue model that cannot capture the reasoning meaning of the multi-hop paths of the knowledge graph and the inconsistent theme of the reply context. |
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| Keywords:dialogue model; knowledge graph; granular computing; attention mechanism |
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