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<p>Reasoning over Temporal Knowledge Graphs (TKGs) presents challenges in modeling the dynamic relationships and evolving behaviors of entities and relations over time. Traditional approaches often treat entities and relations separately, which limits their ability to capture their joint temporal evolution and interactions. To overcome these limitations, REFD (<b>R</b>ecurrent <b>E</b>ncoder and <b>F</b>usion <b>D</b>ecoder) is proposed, a novel framework designed to improve TKG reasoning. The REFD framework consists of two primary components: a recurrent encoder and a fusion decoder. The recurrent encoder incorporates three key modules: (1) the full-domain multi-scale temporal recurrent encoder, which effectively captures temporal dependencies across varying time scales, (2) the entity-relation symbiotic temporal feature deep fusion engine, which integrates temporal features of both entities and relations, and (3) the intelligent temporal feature priority dynamic adjustment mechanism, which adaptively adjusts the importance of different features over time. The fusion decoder, particularly the entity-relation feature Fusion Decoder, combines the temporal features of entities and relations to model their joint evolution, overcoming the limitations of previous methods that model them separately. By jointly capturing the evolving dynamics of entities and relations over time, REFD significantly enhances the accuracy of temporal reasoning tasks. Experimental results show that REFD outperforms existing approaches, offering superior prediction accuracy and better handling of the complexities in TKGs.</p>

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REFD:recurrent encoder and fusion decoder for temporal knowledge graph reasoning

  • 期刊论文

  • 发表日期：

    2025年3月22日

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  • Qian Liu,
  • Siling Feng,
  • MengXing Huang,
  • Uzair Aslam Bhatti

摘要

Reasoning over Temporal Knowledge Graphs (TKGs) presents challenges in modeling the dynamic relationships and evolving behaviors of entities and relations over time. Traditional approaches often treat entities and relations separately, which limits their ability to capture their joint temporal evolution and interactions. To overcome these limitations, REFD (Recurrent Encoder and Fusion Decoder) is proposed, a novel framework designed to improve TKG reasoning. The REFD framework consists of two primary components: a recurrent encoder and a fusion decoder. The recurrent encoder incorporates three key modules: (1) the full-domain multi-scale temporal recurrent encoder, which effectively captures temporal dependencies across varying time scales, (2) the entity-relation symbiotic temporal feature deep fusion engine, which integrates temporal features of both entities and relations, and (3) the intelligent temporal feature priority dynamic adjustment mechanism, which adaptively adjusts the importance of different features over time. The fusion decoder, particularly the entity-relation feature Fusion Decoder, combines the temporal features of entities and relations to model their joint evolution, overcoming the limitations of previous methods that model them separately. By jointly capturing the evolving dynamics of entities and relations over time, REFD significantly enhances the accuracy of temporal reasoning tasks. Experimental results show that REFD outperforms existing approaches, offering superior prediction accuracy and better handling of the complexities in TKGs.