ConvHiA: convolutional network with hierarchical attention for knowledge graph multi-hop reasoning

Knowledge graphs can provide a rich resource for constructing question answering systems and recommendation systems. However, most knowledge graphs still encounter knowledge incompleteness. The path-based approach predicts the unknown relation between pairwise entities based on existing path facts. This approach is one of the most promising approaches for knowledge graph completion. A critical challenge of such approaches is integrating path sequence information to achieve the goal of better reasoning. Existing researches focus more on the features between neighboring entities and relations in a path, ignoring the semantic relations of the whole triple. A single path consists of entities and relations, but triples contain valuable semantic information. Moreover, the importance of different triples on each path is disparate. To address these problems, we propose a method convolutional network with hierarchical attention to complete the knowledge graph. Firstly, we use a convolutional network and bidirectional long short-term memory to extract the features of each triple in the path. Then, we employ a novel hierarchical attention network, including triple-level attention and path-level attention, picking up path features at multiple granularities. In addition, we elaborate a multistep reasoning component that repeats multiple interactions with the hierarchical attention module to obtain more plausible inference evidence. Finally, we predict the relation between query entities and provide the most dominant path to explain our answer. The experimental results show that our method outperforms existing approaches by 1-3% on four datasets.