Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information. To address this problem, it has been recently introduced a promising approach based on jointly embedding logical queries and KGs into a low-dimensional space to identify answer entities. However, existing proposals ignore critical semantic knowledge inherently available in KGs, such as type information. To leverage type information, we propose a novel TypE-aware Message Passing (TEMP) model, which enhances the entity and relation representations in queries, and simultaneously improves generalization, deductive and inductive reasoning. Remarkably, TEMP is a plug-and-play model that can be easily incorporated into existing embedding-based models to improve their performance. Extensive experiments on three real-world datasets demonstrate TEMP's effectiveness.
翻译:关于实际生活知识图(KGs)的多动脉推理是一个极具挑战性的问题,因为传统的子图匹配方法无法处理噪音和缺失的信息。为了解决这一问题,最近采用了一种有希望的方法,其基础是将逻辑查询和KGs联合嵌入一个低维空间,以确定答案实体。然而,现有提案忽视了KGs固有的关键语义知识,例如类型信息。为了利用类型信息,我们提议了一个新型的TypE-觉悟信息传递(TEMP)模式,该模式将加强实体和查询中的关系表达,同时改进一般化、推算和感化推理。值得注意的是,TEMP是一种插接和演模式,可以很容易地纳入现有的嵌入模型,以提高其性能。关于三个真实世界数据集的广泛实验证明了TEMP的有效性。