MemTimes: Temporal Scoping of Facts with Memory Network

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish (US)
Title of host publicationDatabase Systems for Advanced Applications
PublisherSpringer International Publishing
Number of pages17
ISBN (Print)9783030594183
StatePublished - Sep 21 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-15
Acknowledgements: 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).


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