Abstract: Temporal knowledge graphs(TKGs) dynamically record event knowledge in the form of quadruples(subject,relation,object,timestamp),effectively capturing the dynamic evolution of knowledge in the real world.As a result,they have been widely applied in various domains such as recommender systems,large language models,and knowledge-based question answering.However,their inherent incompleteness poses significant challenges for further development and application.Temporal knowledge graph reasoning aims to predict missing event knowledge in TKGs,and has thus attracted considerable attention from both academia and industry.Existing methods for TKG reasoning mainly focus on extracting structural information within graph snapshots and modeling temporal dependencies between them.Nonetheless,they still suffer from two major limitations:1)insufficient handling of noise and redundancy present in the snapshots during the modeling process;2)an overreliance on local temporal patterns within short time windows,while ignoring global temporal dependencies across the entire TKG.To address these issues,this paper proposes GIBformer,a novel temporal knowledge graph reasoning framework that integrates the graph information bottleneck principle with a Transformer architecture.Specifically,it first introduces the graph information bottleneck to compress structural information in TKGs,preserving key information that is highly relevant to downstream prediction tasks while effectively filtering out noise and redundancy.Then,a Transformer with multi-head attention is employed to capture global temporal dependencies across snapshots,while also incorporating local temporal dynamics to enhance the prediction of missing event knowledge.Extensive experiments conducted on four widely-used benchmark datasets demonstrate the effectiveness of the proposed model.

Key words: Temporal knowledge graph, Graph information bottleneck, Transformer, Temporal knowledge graph reasoning