Learning and Deducing Temporal Orders

Wenfei Fan, Resul Tugay, Yaoshu Wang, Min Xie, Muhammad Asif Ali

Research output: Contribution to journalArticlepeer-review


This paper studies how to determine temporal orders on attribute values in a set of tuples that pertain to the same entity, in the absence of complete timestamps. We propose a creator-critic framework to learn and deduce temporal orders by combining deep learning and rule-based deduction, referred to as GATE (Get the lATEst). The creator of GATE trains a ranking model via deep learning, to learn temporal orders and rank attribute values based on correlations among the attributes. The critic then validates the temporal orders learned and deduces more ranked pairs by chasing the data with currency constraints; it also provides augmented training data as feedback for the creator to improve the ranking in the next round. The process proceeds until the temporal order obtained becomes stable. Using real-life and synthetic datasets, we show that GATE is able to determine temporal orders with F-measure above 80%, improving deep learning by 7.8% and rule-based methods by 34.4%.
Original languageEnglish (US)
Pages (from-to)1944-1957
Number of pages14
Issue number8
StatePublished - Apr 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-06-19
Acknowledgements: This work was supported in part by Royal Society Wolfson Research Merit Award WRM/R1/180014, Guangdong Basic and Applied Basic Research Foundation 2022A1515010120 and NSFC 62202313.


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