Learning and Deducing Temporal Orders

Abstract

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%.