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Temporal knowledge subgraph inference based on time-aware relation representation

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Abstract

Temporal knowledge graphs (TKGs) extend traditional knowledge graphs by incorporating temporal information to represent valid time. TKG reasoning involves inferring and predicting missing or future facts within various time intervals, and applications in critical domains include political event prediction and financial analysis. Current TKG reasoning methods rely on path-based or subgraph-based strategies to achieve enhanced interpretability. However, they mostly focus on entity interaction information within paths or subgraphs, and they fail to exploit the semantic and temporal information of relations during reasoning. To comprehensively utilize such information for refining the reasoning process, we propose a subgraph reasoning model based on time-aware relation representation (TiAR) for TKGs. TiAR learns the subgraph structures around query nodes and reasons on facts happening in future timestamps. It encodes the relation features by combining the temporal displacement values between events and extracts the semantic correlations between query and adjacency relations via an attention-based neighborhood feature aggregation module. TiAR extracts a query subgraph of interest by iteratively sampling and diffusing attention through the whole graph. In particular, TiAR includes a path constraint module to further capture the semantic correlations between the structural features of queries and subgraphs. Our evaluation conducted on four benchmark datasets demonstrates that TiAR outperforms the current state-of-the-art models, indicating its effectiveness in achieving improved TKG reasoning. In particular, for the two large ICEWS05-15/18 datasets, our model yields better performance than the latest model. Moreover, The development of TKG reasoning methods is essential for improving the reliability and interpretability of models in such domains, and our work contributes to this area of research.

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Acknowledgements

This research work was partly supported by National Natural Science Foundation of China under grant No. 62271125 and No. 62273071, and by Sichuan Science and Technology Program (No.2022YFG0038 and No.2021YFG0018), and by the Fundamental Research Funds for the Central Universities (No. ZYGX2020ZB034 and No.ZYGX2021J019).

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, No.4, Section 2, North Jianshe Road, Chengdu, 610054, Sichuan, China

    Chong Mu

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-tech Zone, Chengdu, 611731, Sichuan, China

    Lizong Zhang & Ling Tian

  3. Chenji Zhihang (Beijing) Technology Co., Ltd, Beijing, 100072, China

    Yanqing Ma

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  1. Chong Mu
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Correspondence to Lizong Zhang.

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Mu, C., Zhang, L., Ma, Y. et al. Temporal knowledge subgraph inference based on time-aware relation representation. Appl Intell 53, 24237–24252 (2023). https://doi.org/10.1007/s10489-023-04833-9

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