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Semantic-enhanced reasoning question answering over temporal knowledge graphs

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Abstract

Question Answering Over Temporal Knowledge Graphs (TKGQA) is an important topic in question answering. TKGQA focuses on accurately understanding questions involving temporal constraints and retrieving accurate answers from knowledge graphs. In previous research, the hierarchical structure of question contexts and the constraints imposed by temporal information on different sentence components have been overlooked. In this paper, we propose a framework called “Semantic-Enhanced Reasoning Question Answering” (SERQA) to tackle this problem. First, we adopt a pretrained language model (LM) to obtain the question relation representation vector. Then, we leverage syntactic information from the constituent tree and dependency tree, in combination with Masked Self-Attention (MSA), to enhance temporal constraint features. Finally, we integrate the temporal constraint features into the question relation representation using an information fusion function for answer prediction. Experimental results demonstrate that SERQA achieves better performance on the CRONQUESTIONS and ImConstrainedQuestions datasets. In comparison with existing temporal KGQA methods, our model exhibits outstanding performance in comprehending temporal constraint questions. The ablation experiments verified the effectiveness of combining the constituent tree and the dependency tree with MSA in question answering.

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Data Availability

All the datasets gathered from other sources has been publicly available.

Notes

  1. https://github.com/yzhangcs/parser

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Acknowledgements

Funding was provided by Key Research and Development Projects of Shaanxi Province (Grant No: 2020ZDLGY09-05)

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Authors and Affiliations

  1. Xi’an University of Posts and Telecommunications, 618 West Chang’an Street, Xi’an, China

    Chenyang Du & Xiaoge Li

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an, 710121, Shaanxi, China

    Chenyang Du & Xiaoge Li

  3. Pomegranate Technologies Inc., 511 W 25th st, New York, NY, 10001, USA

    Zhongyang Li

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  1. Chenyang Du
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  2. Xiaoge Li
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Correspondence to Xiaoge Li.

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Du, C., Li, X. & Li, Z. Semantic-enhanced reasoning question answering over temporal knowledge graphs. J Intell Inf Syst (2024). https://doi.org/10.1007/s10844-024-00840-5

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