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home / papers / 2023 / 面向链接预测的知识图谱表示学习方法综述

面向链接预测的知识图谱表示学习方法综述

Summary

This survey, written in Chinese by a Fudan University team, covers representation learning for knowledge graph (KG) link prediction. Link prediction means inferring missing entities or relations in incomplete KGs. The authors argue that earlier surveys mostly cover triple-based (binary) embeddings and give little attention to graph neural networks.

They organise the field along two axes:

  • Knowledge representation form: binary relations (triples), N-ary relations (sets of role–value pairs) and hyper-relational facts (a main triple plus qualifier pairs). Each form gets a formal task definition.
  • Modelling approach: translation distance, tensor decomposition, traditional neural networks and GNNs.

The paper describes how each model family is built and traces how representative models moved from binary to N-ary and hyper-relational settings. It also reviews standard datasets and evaluation metrics, compares reported results across the three representation forms, and outlines future directions in model optimisation, representation forms and problem scope. Rather than offering a new method, it serves as a structured map of roughly a decade of work. It may be useful as an entry point for KG completion projects.

Key points

  • Coverage: 35 representation learning models proposed between 2012 and 2021, arranged in a timeline that shows how models evolved from one another. Examples include TransE, TransH, TransR, RESCAL, DistMult, ComplEx, RotatE, TuckER, ConvE, ConvKB, R-GCN, CompGCN, NaLP, HINGE and StarE.
  • Motivation: KGs are badly incomplete. In FreeBase, 70% of person entities have no birthplace and 99% have no ethnicity recorded.
  • Two pipeline styles:
    • Translation, tensor and classic neural models train end-to-end, sharing one scoring function between learning and prediction.
    • GNN models separate the two steps with an encoder–decoder setup.
  • Representation forms:
    • Hyper-relational facts keep a primary triple, with the number of qualifiers equal to the arity N minus 2. With zero qualifiers, the fact reduces to an ordinary triple.
    • This matches how Wikidata, newer DBpedia releases and YAGO 4 (RDF*) store statements.
    • Knowledge hypergraphs are judged too immature for link prediction so far.
  • Bibliographic details: Journal of Software (软件学报), vol. 35, no. 1, pp. 87–117, DOI 10.13328/j.cnki.jos.006902. The issue is dated 2024, but the paper was first published online in August 2023.

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.006902

@article{2023,
  title = {{面向链接预测的知识图谱表示学习方法综述}},
  author = {杜雪盈 and 刘名威 and 沈立炜 and 彭鑫},
  journal = {软件学报 (Journal of Software)},
  year = {2023},
  doi = {10.13328/j.cnki.jos.006902},
  url = {https://doi.org/10.13328/j.cnki.jos.006902}
}

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