Answering natural language questions on knowledge graphs (KGQA) remains a
great challenge in terms of understanding complex questions via multi-hop
reasoning. Previous efforts usually exploit large-scale entity-related text
corpora or knowledge graph (KG) embeddings as auxiliary information to
facilitate answer selection. However, the rich semantics implied in
off-the-shelf relation paths between entities is far from well explored. This
paper proposes improving multi-hop KGQA by exploiting relation paths' hybrid
semantics. Specifically, we integrate explicit textual information and implicit
KG structural features of relation paths based on a novel rotate-and-scale
entity link prediction framework. Extensive experiments on three existing KGQA
datasets demonstrate the superiority of our method, especially in multi-hop
scenarios. Further investigation confirms our method's systematical
coordination between questions and relation paths to identify answer entities.

本文提出了一种利用关系路径的混合语义来改善多跳知识图谱问答（KGQA）的方法，以较大程度的解决理解复杂问题的问题，通过实验证明该方法在多跳场景下的优越性。