MemTimes: Temporal Scoping of Facts with Memory Network

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Conference Paper

Cao, Siyuan
Yang, Qiang
Li, Zhixu
Liu, Guanfeng
Zhang, Detian
Xu, Jiajie

KAUST Department
King Abdullah University of Science and Technology, Jeddah, Saudi Arabia

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This paper works on temporal scoping, i.e., adding time interval to facts in Knowledge Bases (KBs). The existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we propose a novel neural model based on Memory Network to do temporal reasoning among sentences for the purpose of temporal scoping. We design proper ways to encode both semantic and temporal information contained in the mention set of each fact, which enables temporal reasoning with Memory Network. We also find ways to remove the effect brought by noisy sentences, which can further improve the robustness of our approach. The experiments show that this solution is highly effective for detecting temporal scope of facts.

Cao, S., Yang, Q., Li, Z., Liu, G., Zhang, D., & Xu, J. (2020). MemTimes: Temporal Scoping of Facts with Memory Network. Lecture Notes in Computer Science, 70–86. doi:10.1007/978-3-030-59419-0_5

This research is partially supported by Natural Science Foundation of Jiangsu Province (No. BK20191420), National Natural Science Foundation of China (Grant No. 61632016, 61572336, 61572335, 61772356), Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010), and the Open Program of Neusoft Corporation (No. SKLSAOP1801).

Springer Nature

Conference/Event Name
25th International Conference on Database Systems for Advanced Applications, DASFAA 2020


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