Graph Intention Neural Network for Knowledge Graph Reasoning

Reasoning over knowledge graph explores valuable information for amounts of tasks. However, most methods adopt the coarse-grained and single representation of each entity for reasoning, ignoring simultaneously processing various semantics contained in internal information and external information. On the one hand, the surrounding nodes and relations existing in the graph structure express the internal information of the entity, which contains abundant graph context information, but the extracted internal features are still limited. On the other hand, different scenarios as the external information focus on different aspects of the certain entity, meanwhile the external information should have message interaction with the internal information to learn the adaptive embedding, both of which are seldom considered by the existing methods. In this paper, we propose a Graph Intention Neural Network (GINN) for knowledge graph reasoning to explore fine-grained entity representations, which use external-intention and internal-intention simultaneously. For external-intention, a novel constructed matrix is used to calculate the triple-attention that determines the aggregated information to learn different embeddings adapting to the different scenarios. Furthermore, a communication bridge is leveraged to have message interaction between the external information and the internal information. For the internal-intention, the surrounding nodes and relations are integrated to update the entity embedding with the consideration of the interaction features between the external and internal information. The triple-attention can capture relevancy among the reasoning hops, which contributes to figuring out reasonable paths. We evaluate our approach on real-world datasets, achieving better performance compared to the state-of-the-art methods and showing plausible interpretability for the results.