Learning Dynamic and Static Representations for Extrapolation-Based Temporal Knowledge Graph Reasoning

doi:10.1109/TASLP.2024.3485500

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Learning Dynamic and Static Representations for Extrapolation-Based Temporal Knowledge Graph Reasoning

2024-01-01

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DOI：10.1109/TASLP.2024.3485500

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Abstract

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Temporal knowledge graph reasoning aims to predict the missing links (facts) in the future timestamps. However, most existing methods have a common limitation: they focus on learning dynamic representations of temporal knowledge graphs and rarely consider static characteristics that remain unchanged over time. To address the above issues, we propose to learn the dynamic and static representations for temporal knowledge graph reasoning (DSTKG), which introduces two latent variables to capture the dynamic and static characteristics of entities in temporal knowledge graphs. First, we use a Bi-GRU-based inference network to learn the static latent representation of historical facts and a nonlinear discrete-time transition-based inference network to learn the dynamic latent representation. Then, we sample the latent variables multiple times using re-parameterization tricks to obtain high-quality embeddings and make predictions in the future timestamps. The empirical results on four benchmark datasets show that our model is more effective than state-of-the-art approaches. Compared with the strong baseline model DBKGE (RotatE), the proposed model achieves performance improvements of 2.69%, 1.59%, 1.18% and 1.22% on Yago11k, Wikidata12k, ICEWS14 and ICEWS05-15 respectively, regarding the evaluation metric MRR.

Keywords:

Cognition

Knowledge graphs

Predictive models

Semantics

Computational modeling

Adaptation models

Speech processing

Interpolation

Gaussian distribution

Extrapolation

Knowledge graph embedding