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        <title>Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs</title>
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    <name type="personal">
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    <abstract>Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years. In the generation of temporal queries, existing KGQA methods ignore the fact that some intrinsic connections between events can make them temporally related, which may limit their capability. We systematically analyze the possible interpretation of temporal constraints and conclude the interpretation structures as the Semantic Framework of Temporal Constraints, SF-TCons. Based on the semantic framework, we propose a temporal question answering method, SF-TQA, which generates query graphs by exploring the relevant facts of mentioned entities, where the exploring process is restricted by SF-TCons. Our evaluations show that SF-TQA significantly outperforms existing methods on two benchmarks over different knowledge graphs.</abstract>
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%0 Conference Proceedings
%T Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs
%A Ding, Wentao
%A Chen, Hao
%A Li, Huayu
%A Qu, Yuzhong
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ding-etal-2022-semantic
%X Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years. In the generation of temporal queries, existing KGQA methods ignore the fact that some intrinsic connections between events can make them temporally related, which may limit their capability. We systematically analyze the possible interpretation of temporal constraints and conclude the interpretation structures as the Semantic Framework of Temporal Constraints, SF-TCons. Based on the semantic framework, we propose a temporal question answering method, SF-TQA, which generates query graphs by exploring the relevant facts of mentioned entities, where the exploring process is restricted by SF-TCons. Our evaluations show that SF-TQA significantly outperforms existing methods on two benchmarks over different knowledge graphs.
%R 10.18653/v1/2022.emnlp-main.122
%U https://aclanthology.org/2022.emnlp-main.122
%U https://doi.org/10.18653/v1/2022.emnlp-main.122
%P 1867-1877

Markdown (Informal)

[Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs](https://aclanthology.org/2022.emnlp-main.122) (Ding et al., EMNLP 2022)

• Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs (Ding et al., EMNLP 2022)

ACL

• Wentao Ding, Hao Chen, Huayu Li, and Yuzhong Qu. 2022. Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1867–1877, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.