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Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion

Author

Listed:
  • Xuechen Zhao

    (School of Data and Computer Science, Shandong Women’s University, Jinan 250300, China)

  • Jinfeng Miao

    (School of Data and Computer Science, Shandong Women’s University, Jinan 250300, China)

  • Fuqiang Yang

    (School of Data and Computer Science, Shandong Women’s University, Jinan 250300, China)

  • Shengnan Pang

    (School of Journalism and Communication, Tsinghua University, Beijing 100190, China)

Abstract

Knowledge graphs (KGs) have become a cornerstone for structuring vast amounts of information, enabling sophisticated AI applications across domains. The progression to temporal knowledge graphs (TKGs) introduces time as an essential dimension, allowing for a dynamic representation of entity relationships. Despite their potential, TKGs often suffer from incompleteness, necessitating the development of temporal knowledge graph completion (TKGC) techniques. These methods, particularly focusing on interpolation within the known timeframe, aim to infer missing temporal facts and enhance the predictive capabilities of TKGs. The prevalent reliance on Euclidean space modeling in TKGC methods presents challenges in capturing the complex, hierarchical, and time-varying nature of TKGs. To overcome these limitations, we introduced the attention-based geometry interaction embedding (ATGIE) method, a novel approach that leverages the strengths of multiple geometric spaces, i.e., Euclidean, hyperbolic, and hypersphere, to model the intricacies of TKGs more effectively. ATGIE employs an attention mechanism to dynamically weigh the contributions of different geometric spaces, allowing it to adaptively form reliable spatial structures based on interactive geometric information. This multi-space modeling not only captures the diverse relationships within TKGs but also facilitates a nuanced understanding of how entities and their relationships evolve over time. Through extensive experiments, we demonstrate ATGIE’s superiority in TKGC tasks, showcasing its improvement over existing methods, robustness to noise, and sensitivity to temporal dynamics. The results highlight ATGIE’s potential to advance the state-of-the-art in TKGC, offering a promising direction for research and application in the field.

Suggested Citation

  • Xuechen Zhao & Jinfeng Miao & Fuqiang Yang & Shengnan Pang, 2024. "Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion," Mathematics, MDPI, vol. 12(13), pages 1-15, June.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2022-:d:1425347
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    References listed on IDEAS

    as
    1. Ke Sun & Shuo Yu & Ciyuan Peng & Yueru Wang & Osama Alfarraj & Amr Tolba & Feng Xia, 2022. "Relational Structure-Aware Knowledge Graph Representation in Complex Space," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
    2. Yanying Mao & Honghui Chen, 2021. "Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types," Mathematics, MDPI, vol. 9(16), pages 1-11, August.
    3. Pengfei Zhang & Dong Chen & Yang Fang & Xiang Zhao & Weidong Xiao, 2022. "CIST: Differentiating Concepts and Instances Based on Spatial Transformation for Knowledge Graph Embedding," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    4. Xiangwen Liu & Shengyu Mao & Xiaohan Wang & Jiajun Bu, 2023. "Generative Transformer with Knowledge-Guided Decoding for Academic Knowledge Graph Completion," Mathematics, MDPI, vol. 11(5), pages 1-12, February.
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