Abstract
A temporal knowledge graph (TKG) comprises facts aligned with timestamps. Question answering over TKGs (TKGQA) finds an entity or timestamp to answer a question with certain temporal constraints. Current studies assume that the questions are fully annotated before being fed into the system, and treat question answering as a link prediction task. Moreover, the process of choosing answers is not interpretable due to the implicit reasoning in the latent space. In this paper, we propose a semantic parsing based method, namely AE-TQ, which leverages abstract meaning representation (AMR) for understanding complex questions, and produces question-oriented semantic information for explicit and effective temporal reasoning. We evaluate our method on CronQuestions, the largest known TKGQA dataset, and the experiment results demonstrate that AE-TQ empirically outperforms several competing methods in various settings.
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Acknowledgements
This work is supported by Guangzhou Basic and Applied Basic Research Foundation (Grant No. 202201020131), GuangDong Basic and Applied Basic Research Foundation 2019B1515120048, and NSFC under grants No.61872446.
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Long, S., Liao, J., Yang, S., Zhao, X., Lin, X. (2022). Complex Question Answering Over Temporal Knowledge Graphs. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_6
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