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TKGR-GPRSCL: Enhance Temporal Knowledge Graph Reasoning with Graph Structure-Aware Path Representation and Supervised Contrastive Learning

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 15360))

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Abstract

Temporal knowledge graph reasoning (TKGR) aims to infer new temporal fact knowledge based on existing ones, which has become a major concern due to its wide demands, especially for time-sensitive modeling scenarios. However, existing temporal knowledge graph representation learning models usually have challenges in capturing complex graph structure-aware information and discriminating different relation expressions for unknown query prediction, which potentially limits the model performance on the TKGR task. In this paper, we propose a novel TKGR model named TKGR-GPRSCL. Specifically, we first introduce a maximum entropy-based random walk sampler, which can sample sufficient paths containing complicated graph structure-aware information in the TKG in form of path embedding. Then, we model the preference of different paths to the target entity, and the correlations between the target entity and its temporal property depending on transformations in the complex plane for encoding temporal fact. Finally, we advance supervised contrastive learning of relation patterns in light of the discrimination of different relation expressions to be preferably leveraged to predict entities for testing the queries, for those unknown queries in particular. We evaluate TKGR-GPRSCL on three TKGR benchmark datasets, and experimental results demonstrate that our model achieves superior performance compared to competitive baselines  (All the data and codes are publicly available athttps://github.com/shengyp/TKGR-GPRSCL).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62202075, No. 62171111, No. 62376058, No. 62376043, No. 62002052), the Natural Science Foundation of Chongqing, China (No. CSTB2022N SCQ-MSX1404, No. CSTB2023NSCQ-MSX0091), Fundamental Research Funds for the Central Universities (No. SWU-KR24008), Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education (No. DSIE202206), and the State Key Laboratory of Public Big Data, Guizhou University (No. PBD2024-0501).

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Lizhu Xiong & Yongpan Sheng

  2. School of Big Data and Software Engineering, Chongqing University, Chongqing, 401331, China

    Yongpan Sheng

  3. School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, China

    Lirong He

Authors
  1. Lizhu Xiong

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  2. Yongpan Sheng

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  3. Lirong He

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Correspondence toYongpan Sheng.

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Editors and Affiliations

  1. University of Macau, Macao, China

    Derek F. Wong

  2. Fudan University, Shanghai, China

    Zhongyu Wei

  3. Harbin Institute of Technology, Harbin, China

    Muyun Yang

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Xiong, L., Sheng, Y., He, L. (2025). TKGR-GPRSCL: Enhance Temporal Knowledge Graph Reasoning with Graph Structure-Aware Path Representation and Supervised Contrastive Learning. In: Wong, D.F., Wei, Z., Yang, M. (eds) Natural Language Processing and Chinese Computing. NLPCC 2024. Lecture Notes in Computer Science(), vol 15360. Springer, Singapore. https://doi.org/10.1007/978-981-97-9434-8_16

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