TAGAT: Type-Aware Graph Attention neTworks for reasoning over knowledge graphs

With the rapid development of knowledge graphs (KGs), various AI-related applications have been affected positively. However, even though some KGs are relatively large, they still suffer from incompleteness. This has mighty promoted the development of reasoning over KGs to complement them. However, most existing reasoning methods only focus on semantics in the KG, ignoring potential or valuable information hidden in it, the most typical ones are neighborhood information and type information. This limits not only the reasoning performance but also the interpretability of the embedding space. To this end, we propose a Type-Aware Graph Attention neTworks (TAGAT) for the reasoning task over KGs. Except for combining type-related information during the embedding process, TAGAT further adopts a hierarchical attention mechanism to realize the perception of type and neighborhood information meticulously. For each entity, different attention levels respectively focus on considering the contribution of different relations, different types under each relation and different entity of each type under each relation. Moreover, the embedding space of TAGAT is constrained by the type information and naturally has better type-related interpretability, which greatly complements the defects of existing KGC models. Extensive experiments validate the high reasoning performance and the relatively ideal interpretability of our model.