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时序知识图谱表示与推理的研究进展与趋势

Summary

This survey examines a limitation of conventional knowledge graphs. Static graphs do not track how facts change, so they suit poorly the domains where knowledge is revised often. Temporal knowledge graphs (TKGs) address this by attaching time information to each fact, written as a quadruple (s, r, o, τ). The authors catalogue more than 30 TKG representation and reasoning models. They organise them with a unified framework of three core modules: a base embedding layer, a temporal-information component, and a reasoning component. A fourth design dimension covers adaptations to special scenarios. The paper defines four inference tasks: entity, relation, time and future-event prediction. Each model is classified along these axes, which reveals how the field has developed. Early approaches extended translational or bilinear static embeddings with explicit time terms. After 2021, the dominant approach shifted toward graph-convolutional encoders combined with history extraction. The review ends with likely research directions. It is useful as an orientation map for newcomers and as a design guide for building new TKG models.

Key points

  • Origin of the field. The TKG concept was introduced in 2016 by Jiang et al. together with t-TransE, the first temporal encoding model. Other early baselines include TransE-TAE and X-TransE, the latter being the first model able to predict time.
  • Framework axes.
    • Base embeddings are built either on semantic correlation (TransE, SimplE, DistMult, RotatE) or on structural correlation (GCN, R-GCN).
    • Time is handled either through explicit modelling or through history extraction (RNN, LSTM, Hawkes processes).
    • Reasoning is semantic, structural, probabilistic or hybrid.
  • Special scenarios. Three are identified: incremental/online learning (e.g., TIE, CEN), few- or zero-shot settings (e.g., MetaTKG), and open-world settings with unseen entities (e.g., TITer).
  • Model comparison. Table 1 compares models from venues such as EMNLP, ICLR, AAAI, SIGIR and ACL. Examples include RE-Net, RE-GCN, CyGNet, BoxTE, ChronoR, DyERNIE, CENET and HGLS.
  • Base-embedding choice matters. DE-SimplE performs best with SimplE as its base, and TA-DistMult clearly outperforms TA-TransE. The paper also notes a growing number of models that skip a pretrained base embedding and start from random initialisation.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages · Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.13328/j.cnki.jos.007093

@article{2024,
  title = {{时序知识图谱表示与推理的研究进展与趋势}},
  author = {王俞涵 and 陈子阳 and 赵翔 and 谭真 and 肖卫东 and 程学旗},
  journal = {软件学报 (Journal of Software)},
  year = {2024},
  doi = {10.13328/j.cnki.jos.007093},
  url = {https://doi.org/10.13328/j.cnki.jos.007093}
}

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