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.

本文提出了一种新颖的基于 TypE-aware 信息传递（TEMP）模型，它通过加强查询中的实体和关系表示，并同时提高了概括推理、演绎推理和归纳推理，来利用知识库中关键的语义知识，从而提高现有嵌入式模型的性能。实验结果表明其有效性。