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TSA-Net: a temporal knowledge graph completion method with temporal-structural adaptation

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Abstract

Temporal Knowledge Graph Completion (TKGC) aims to infer missing facts in Temporal Knowledge Graphs (TKGs), where facts are stored along with significant temporal information. However, existing TKGC methods only consider message passing on pairwise relations and fail to capture the complex temporal structural dependencies at the levels of time, predicate and entity in TKGs. To fill this gap, we collect high-frequency patterns in TKGs using mathematical statistics and propose a Temporal-Structural Adaptation Network that is equipped with three specific components, time-component, pred-component, and ent-component, as well as one general component, res-component. Concretely, specific components utilize the time consistency pattern to capture facts with significant regularity in time, and complex structural dependencies in TKGs are handled through predicate concurrency and entity collaboration. Moreover, considering low-frequency and nonoccurrence facts, an additional general component is introduced to make predictions on all entities. The outputs of different components are adaptively fused to vote for the final result. Extensive experiments on six benchmarks demonstrate that our method outperforms state-of-the-art baselines.

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Data Availibility Statement

The datasets generated and analyzed during the current study are available in the TSA-Net repository, https://github.com/jack012138/TSA-Net.

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Acknowledgements

This work was supported by the Natural Science Foundation of Guangdong Province, China No. 2022A1515010148 and National Natural Science Foundation of China No. 62177015.

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, 55 Zhongshan Ave. W, Tianhe District, Guangzhou, 510631, Guangdong, China

    Ruzhong Xie, Bosong Huang, Jing Xiao & Jin Huang

  2. Research Institute of China Telecom Corporate Ltd., Guangzhou, 510000, Guangdong, China

    Ke Ruan & Weihao Yu

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  1. Ruzhong Xie
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  2. Ke Ruan
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  4. Weihao Yu
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  6. Jin Huang
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Correspondence to Jin Huang.

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Xie, R., Ruan, K., Huang, B. et al. TSA-Net: a temporal knowledge graph completion method with temporal-structural adaptation. Appl Intell 54, 10320–10332 (2024). https://doi.org/10.1007/s10489-024-05734-1

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