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.
@article{arxiv.2209.00870,
title = {Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs},
author = {Zile Qiao and Wei Ye and Tong Zhang and Tong Mo and Weiping Li and Shikun Zhang},
journal= {arXiv preprint arXiv:2209.00870},
year = {2022}
}