Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs
Yao Zhang, Peiyao Li, Hongru Liang, Adam Jatowt, Zhenglu Yang
Abstract
Current Question Answering over Knowledge Graphs (KGQA) task mainly focuses on performing answer reasoning upon KGs with binary facts. However, it neglects the n-ary facts, which contain more than two entities. In this work, we highlight a more challenging but under-explored task: n-ary KGQA, i.e., answering n-ary facts questions upon n-ary KGs. Nevertheless, the multi-hop reasoning framework popular in binary KGQA task is not directly applicable on n-ary KGQA. We propose two feasible improvements: 1) upgrade the basic reasoning unit from entity or relation to fact, and 2) upgrade the reasoning structure from chain to tree. Therefore, we propose a novel fact-tree reasoning framework, FacTree, which integrates the above two upgrades. FacTree transforms the question into a fact tree and performs iterative fact reasoning on the fact tree to infer the correct answer. Experimental results on the n-ary KGQA dataset we constructed and two binary KGQA benchmarks demonstrate the effectiveness of FacTree compared with state-of-the-art methods.- Anthology ID:
- 2022.findings-acl.66
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 788–802
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.66
- DOI:
- 10.18653/v1/2022.findings-acl.66
- Cite (ACL):
- Yao Zhang, Peiyao Li, Hongru Liang, Adam Jatowt, and Zhenglu Yang. 2022. Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs. In Findings of the Association for Computational Linguistics: ACL 2022, pages 788–802, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs (Zhang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.findings-acl.66.pdf