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GLANet: temporal knowledge graph completion based on global and local information-aware network

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Abstract

Knowledge graph completion (KGC) has been widely explored, but the task of temporal knowledge graph completion (TKGC) for predicting future events is far from perfection. Some embedding-based approaches have achieved significant results on the TKGC task by modeling the structural information of each temporal snapshot and the evolution between temporal snapshots. However, due to the uneven distribution of data in knowledge graphs (KGs), models that only utilize local structure and time series information suffer from information sparsity, resulting in some entities failing to obtain a better embedding representation due to less available information. Moreover, existing methods usually do not distinguish between the time span and frequency of historical information, which reduces the performance of link prediction. For this reason, we propose the G lobal and L ocal Information-A ware Net work (GL-ANet) to capture both global and local information. In particular, to model global information, we capture global structural information of entities across time using a global neighborhood aggregator to enrich the representation of entities; global historical information is obtained based on the frequency and time span of historical facts, focusing on recent and frequent events rather than all historical events to suggest the performance of link prediction; to model local information, we propose a two-layer attention network to capture local structural information at each timestamp, using a gating mechanism and GRU to capture local evolution information. Extensive experiments demonstrate the effectiveness of our model, achieving significant improvements and outperforming state-of-the-art models on five benchmark datasets.

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Acknowledgements

This work was supported by the Natural Science Foundation of Fujian, China(No. 2021J01619), the National Natural Science Foundation of China(No.61672159).

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Authors and Affiliations

  1. College of Computer and Data Science, Fuzhou University, Fujian, 350108, China

    Jingbin Wang, Xinyu Lin, Hao Huang, Xifan Ke, Renfei Wu, Changkai You & Kun Guo

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  1. Jingbin Wang
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Correspondence to Kun Guo.

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Appendix A: Datasets with different splitting ratios

Appendix A: Datasets with different splitting ratios

The dataset is further split into \(\mathcal {E}_{\text {train}}\), \(\mathcal {E}_{\text {valid}}\), and \(\mathcal {E}_{\text {test}}\) with a 70%, 10%, and 20% and 60%, 10%, and 30% ratio, respectively, and the other experimental settings are consistent with Tables 3 and 4 in Section 4.2. Tables 7 and 8 show the results of the link prediction experiments for datasets split at 70%, 10%, and 20%. Tables 9 and 10 show the results of the link prediction experiments for the datasets split at 60%, 10%, and 30%. The results show that With different split ratios, GLANet w.GT produces good results in all datasets, proving the effectiveness of our method.

Table 7 Results of link prediction under 70%, 10% and 20% proportional division of the datasets
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Table 8 Results of link prediction under 70%, 10% and 20% proportional division of the datasets
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Table 9 Results of link prediction under 60%, 10% and 30% proportional division of the datasets
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Table 10 Results of link prediction under 60%, 10% and 30% proportional division of the datasets
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Furthermore, the 20% or 30% scale test set requires longer-term prediction of facts than the 10% scale test set, so the experimental results of both the model in this paper and the baseline model on the 20% or 30% scale test set show a small decrease compared to the 10% results (Tables 3 and 4 in Section 4.2), but the relative performance between the algorithms remains generally unchanged.

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Wang, J., Lin, X., Huang, H. et al. GLANet: temporal knowledge graph completion based on global and local information-aware network. Appl Intell 53, 19285–19301 (2023). https://doi.org/10.1007/s10489-023-04481-z

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