.png){kind=icon}
.png){kind=icon}
Return
Extrapolation Reasoning on Temporal Knowledge Graphs via Temporal Dependencies Learning
.png){kind=icon}
OA
AI
DOI：10.1049/cit2.70013.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
extrapolation
link prediction
temporal knowledge graph reasoning
Journal
IF:
7.3
Papers:
653
Citations:
2.4K