Title | ||
---|---|---|
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs. |
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 corpus 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. |
Year | Venue | DocType |
---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Volume | Citations | PageRank |
Proceedings of the 29th International Conference on Computational Linguistics | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Zile Qiao | 1 | 0 | 0.34 |
Wei Ye | 2 | 8 | 6.49 |
Zhang, Tong | 3 | 7126 | 611.43 |
Tong Mo | 4 | 13 | 3.68 |
Weiping Li | 5 | 2 | 2.73 |
Shikun Zhang | 6 | 55 | 21.40 |