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.
翻译:在知识图(KGQA)上回答自然语言问题,在通过多机会推理理解复杂问题方面,仍然是一项巨大的挑战。以前的努力通常利用与实体有关的大规模文本公司或知识图(KG)嵌入作为辅助信息,以便利回答选择。然而,各实体之间现成的关系路径所隐含的丰富的语义远未得到充分探讨。本文件建议通过利用关系路径的混合语义来改进多机会KGQA。具体地说,我们根据新的旋转和规模实体链接预测框架,整合明确的文字信息以及关系路径的隐含KG结构特征。对现有三个KGQA数据集的广泛实验显示了我们方法的优越性,特别是在多机会情景中。进一步的调查证实了我们的方法在问题和确定答复实体的关系路径之间系统协调。