Extrapolation Reasoning on Temporal Knowledge Graphs via Temporal Dependencies Learning

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ye Wang, Binxing Fang, Shuxian Huang, Kai Chen, Yan Jia, Aiping Li
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引用次数: 0

Abstract

Extrapolation on Temporal Knowledge Graphs (TKGs) aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order. The temporally adjacent facts in TKGs naturally form event sequences, called event evolution patterns, implying informative temporal dependencies between events. Recently, many extrapolation works on TKGs have been devoted to modelling these evolutional patterns, but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns. However, the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent. To this end, a Temporal Relational Context-based Temporal Dependencies Learning Network (TRenD) is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns, especially those temporal dependencies caused by interactive patterns of relations. Trend incorporates a semantic context unit to capture semantic correlations between relations, and a structural context unit to learn the interaction pattern of relations. By learning the temporal contexts of relations semantically and structurally, the authors gain insights into the underlying event evolution patterns, enabling to extract comprehensive historical information for future prediction better. Experimental results on benchmark datasets demonstrate the superiority of the model.

Abstract Image

基于时间依赖学习的时间知识图外推推理
时间知识图外推(TKGs)旨在从一组按时间顺序排列的历史知识图中预测未来的知识。tkg中的时间相邻事实自然形成事件序列,称为事件演化模式,暗示事件之间的信息时间依赖性。最近,许多关于tkg的外推工作都致力于对这些进化模式进行建模,但由于大多数现有工作仅仅依赖于将这些模式编码为实体表示,而忽略了进化模式关系所隐含的重要信息,因此这项任务还远远没有解决。然而,作者意识到这些事件演化模式关系中固有的时间依赖性可以在一定程度上指导后续事件的预测。为此,本文提出了一种基于时间关系上下文的时间依赖学习网络(TRenD),探索关系的时间背景,以便更全面地学习事件演化模式,特别是由关系交互模式引起的时间依赖。Trend集成了一个语义上下文单元来捕获关系之间的语义相关性,以及一个结构上下文单元来学习关系的交互模式。通过在语义和结构上学习关系的时间上下文,作者可以深入了解潜在的事件演变模式,从而能够更好地提取全面的历史信息,以便更好地预测未来。在基准数据集上的实验结果证明了该模型的优越性。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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