Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs

• URL: http://arxiv.org/abs/2209.00870v1
• Date: Fri, 2 Sep 2022 08:07:37 GMT
• Title: Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
• Authors: Zile Qiao, Wei Ye, Tong Zhang, Tong Mo, Weiping Li, Shikun Zhang
• Abstract summary: This paper proposes improving multi-hop KGQA by exploiting relation paths' hybrid semantics. We integrate explicit textual information and implicit KG structural features of relation paths based on a novel rotate-and-scale entity link prediction framework.
• Score: 31.088325888508137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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