TCPN: Temporal Pyramidal Recurrent Network with Contrastive Learning for Temporal Knowledge Graph Reasoning

Le résumé fourni par la source

Temporal Knowledge Graphs (TKGs) serve as crucial tools for representing dynamic changes in the real world. Extrapolation reasoning within TKGs aims to predict entirely unknown future facts based on limited historical data, offering considerable practical value across various fields. However, existing methods generally focus on the recurrence and periodicity of historical facts, while overlooking the dynamic interactions associated with future facts. Moreover, these methods fail to capture historical evolutionary patterns, which grow increasingly complex as historical data accumulates. To this end, we propose TCPN, a novel Temporal Pyramidal Recurrent Network with contrastive learning for TKG extrapolation reasoning. Specifically, TCPN leverages a temporal pyramidal recurrent network to capture historical dependencies across multiple temporal scales, thereby refining temporal feature representations over extended time spans. Furthermore, TCPN seamlessly integrates contrastive learning to effectively align historical information with query semantics relevant to future facts. Lastly, we incorporate an adaptive time-aware mechanism, which uniformly models long-short term dependencies in time series with different granularities, explicitly fusing temporal feature information. Extensive experiments on four widely used TKG datasets show that TCPN significantly outperforms state-of-the-art methods across all metrics.

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