Paper Info
Title
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning
Abstract
A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG sequences. One key of this task is to mine and understand evolutional patterns of facts from these sequences. The evolutional patterns are complex in two aspects, length-diversity and time-variability. Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. Furthermore, these models are all trained offline, which cannot well adapt to the changes of evolutional patterns from then on. Thus, we propose a new model, called Complex Evolutional Network (CEN), which uses a length-aware Convolutional Neural Network (CNN) to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. Besides, we propose to learn the model under the online setting so that it can adapt to the changes of evolutional patterns over time. Extensive experiments demonstrate that CEN obtains substantial performance improvement under both the traditional offline and the proposed online settings.
Year
DOI
Venue
2022
10.18653/v1/2022.acl-short.32
PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022): (SHORT PAPERS), VOL 2
DocType
Volume
Citations 
Conference
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
0
PageRank 
References 
Authors
0.34
5
10
Name
Order
Citations
PageRank
Zixuan Li122.48
Saiping Guan293.94
Xiaolong Jin354656.20
Weihua Peng400.68
Yajuan Lyu500.34
Yong Zhu600.34
Long Bai7203.82
Wei Li888393.88
Jiafeng Guo91737102.17
Xueqi Cheng103148247.04