论文标题
利用与知识图回答的多跳问题的关系路径的混合语义
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
论文作者
论文摘要
在知识图上回答自然语言问题（KGQA）仍然是通过多跳推理理解复杂问题的巨大挑战。以前的努力通常利用大规模实体相关的文本语料库或知识图（kg）作为辅助信息来促进答案选择。但是，实体之间隐含的富裕语义远未得到很好的探索。本文建议通过利用关系路径的混合语义来改善多跳kgqa。具体而言，我们基于新颖的旋转和规模的实体链接链接预测框架，整合了明确的文本信息和关系路径的隐式kg结构特征。在三个KGQA数据集上进行的广泛实验证明了我们方法的优越性，尤其是在多跳场景中。进一步的调查证实了我们方法在问题和关系路径之间的系统协调，以识别答案实体。
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.
