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    <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.</abstract>
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%0 Conference Proceedings
%T Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs
%A Zhang, Yao
%A Li, Peiyao
%A Liang, Hongru
%A Jatowt, Adam
%A Yang, Zhenglu
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhang-etal-2022-fact
%X 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.
%R 10.18653/v1/2022.findings-acl.66
%U https://aclanthology.org/2022.findings-acl.66
%U https://doi.org/10.18653/v1/2022.findings-acl.66
%P 788-802

Markdown (Informal)

[Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs](https://aclanthology.org/2022.findings-acl.66) (Zhang et al., Findings 2022)

• Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs (Zhang et al., Findings 2022)

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.