Skip to main content
SpringerLink
Log in

Task-related network based on meta-learning for few-shot knowledge graph completion

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Knowledge graph (KG) is a powerful tool in many areas, but it is impossible to take in all knowledge during construction for the complexity of relations among natural entities. In practical terms, many relations within KG have few samples to learn from, so few-shot KG completion matters. Existing approaches generally complete KGs by learning either static representations of entities and relations or the dynamic representations of fixed model parameters, while ignoring the importance of dynamic fine-tuning of model parameters for rapidly mining task-related information. In this paper, we propose a novel task-related network based on meta-learning (MTRN) for few-shot knowledge graph completion. Firstly, a task-related neighbor-aware encoder, which can distinguish the contributions of different neighbors, is designed to mine the task-related information of neighbors from the heterogeneous background graph and to enhance task entity representations. Secondly, a self-attention entity-pair encoder is proposed to generate the representations of references by modeling the interaction of head and tail entities. Then MTRN models references, that is entity pairs in the reference set, through a matching network to differentiate the contributions of different entity pairs to task relations and obtain the task-related representations with fine-grained semantics. When performing different knowledge graph completion tasks, MTRN can extract the features of few-shot references in a meta-learning way to dynamically fine-tune model parameters, making MTRN quickly adapt to diverse tasks in few-shot setting. The evaluation on two public datasets of knowledge graph completion shows that MTRN achieves competitive completion performance under different few-shot sizes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Guo Q, Zhuang F, Qin C, Zhu H, Xie X, Xiong H, He Q (2020) A survey on knowledge graph-based recommender systems. IEEE Trans Knowl Data Eng

  2. Huang W, Wu J, Song W, Wang Z (2022) Cross attention fusion for knowledge graph optimized recommendation. Appl Intell 1–10

  3. Liu D, Lian J, Liu Z, Wang X, Sun G, Xie X (2021) Reinforced anchor knowledge graph generation for news recommendation reasoning. In: tProceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 1055–1065

  4. Bosselut A, Le Bras R, Choi Y (2021) Dynamic neuro-symbolic knowledge graph construction for zero-shot commonsense question answering. In: AAAI, pp 4923–4931

  5. Cao X, Liu Y (2022) Relmkg: reasoning with pre-trained language models and knowledge graphs for complex question answering. Appl Intell, pp 1–15

  6. Guo Q, Wang X, Zhu Z, Liu P, Xu L (2022) A knowledge inference model for question answering on an incomplete knowledge graph. Appl Intell, pp 1–13

  7. Kejriwal M, Szekely P (2017) Knowledge graphs for social good: An entity-centric search engine for the human trafficking domain. IEEE Trans Big Data

  8. Wang Y, Xu X, Hong Q, Jin J, Wu T (2021) Top-k star queries on knowledge graphs through semantic-aware bounding match scores. Knowl-Based Syst 213:106655

    Article  Google Scholar 

  9. Feng J, Wei Q, Cui J, Chen J (2022) Novel translation knowledge graph completion model based on 2d convolution. Appl Intell 52(3):3266–3275

    Article  Google Scholar 

  10. Huang J, Lu T, Zhu J, Yu W, Zhang T (2022) Multi-relational knowledge graph completion method with local information fusion. Appl Intell 52(7):7985–7994

    Article  Google Scholar 

  11. Shao P, Zhang D, Yang G, Tao J, Che F, Liu T (2022) Tucker decomposition-based temporal knowledge graph completion. Knowl-Based Syst 238:107841

    Article  Google Scholar 

  12. Wang Q, Ji Y, Hao Y, Cao J (2020) GRL: Knowledge graph completion with gan-based reinforcement learning. Knowl-Based Syst 209:106421

    Article  Google Scholar 

  13. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inf Process Syst 26

  14. Ji G, He S, Xu L, Liu K, Zhao J (2015) Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers), pp 687–696

  15. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI conference on artificial intelligence

  16. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI conference on artificial intelligence, volume 28

  17. Xiong W, Yu M, Chang S, Guo X, WY (2018) One-shot relational learning for knowledge graphs. In: EMNLP

  18. Zhang C, Yao H, Huang C, Jiang M, Li Z, Chawla NV (2020) Few-shot knowledge graph completion. Proceedings of the AAAI conference on artificial intelligence 34:3041–3048

    Article  Google Scholar 

  19. Chen M, Zhang W, Zhang W, Chen Q, Chen H (2019) Meta relational learning for few-shot link prediction in knowledge graphs. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 4217–4226

  20. Ma R, Li Z, Ma Y, Wu H, Yu M, Zhao L (2022) Adaptive attentional network for few-shot relational learning of knowledge graphs. Appl Sci 12(9):4284

    Article  Google Scholar 

  21. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26

  22. Nickel M, Tresp V, Kriegel H-P (2011) A three-way model for collective learning on multi-relational data. In: Icml

  23. Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, PMLR, pp 2071–2080

  24. Socher R, Chen D, Manning CD, Ng A (2013) Reasoning with neural tensor networks for knowledge base completion. Adv Neural Inf Process Syst 26

  25. Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI conference on artificial intelligence volume 32

  26. Dai G, Wang X, Zou X, Liu C, Cen S (2022) Mrgat: multi-relational graph attention network for knowledge graph completion. Neural Netw 154:234–245

    Article  Google Scholar 

  27. Li Z, Zhao Y, Zhang Y, Zhang Z (2022) Multi-relational graph attention networks for knowledge graph completion. Knowl-Based Syst 251:109262

    Article  Google Scholar 

  28. Wang J, Zhu C, Zhu W (2022) Dynamic embedding graph attention networks for temporal knowledge graph completion. In: Knowledge science, engineering and management: 15th international conference, KSEM 2022, Singapore, August 6–8, 2022, Proceedings, Part I, Springer, pp 722–734

  29. Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611

    Article  Google Scholar 

  30. Xie Q, Ma X, Dai Z, Hovy E (2017) An interpretable knowledge transfer model for knowledge base completion. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 950–962

  31. Sung F, Yang Y, Zhang L, Xiang T, Torr PH, Hospedales TM (2018) Learning to compare: Relation network for few-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1199–1208

  32. Vartak M, Thiagarajan A, Miranda C, Bratman J, Larochelle H (2017) A meta-learning perspective on cold-start recommendations for items. Adv Neural Inf Process Syst 30

  33. Geng R, Li B, Li Y, Zhu X, Jian P, Sun J (2019) Induction networks for few-shot text classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3904–3913

  34. Dong X, Zhu L, Zhang D, Yang Y, Wu F (2018) Fast parameter adaptation for few-shot image captioning and visual question answering. In: Proceedings of the 26th ACM international conference on multimedia, pp 54–62

  35. Koch G, Zemel R, Salakhutdinov R et al (2015) Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, volume 2, page 0. Lille

  36. Sun S, Sun Q, Zhou K, Lv T (2019) Hierarchical attention prototypical networks for few-shot text classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 476–485

  37. Vinyals O, Blundell C, Lillicrap T, Wierstra D et al (2016) Matching networks for one shot learning. Adv Neural Inf Process Syst 29

  38. Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning, PMLR, pp 1126–1135

  39. Geng R, Li B, Li Y, Zhu X, Jian P, Sun J (2018) Attentive task-agnostic meta-learning for few-shot text classification. In: In NeurIPS Meta-Learning Workshop)

  40. Nichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. arXiv:1803.02999

  41. Sheng J, Guo S, Chen Z, Yue J, Wang L, Liu T, Xu H (2020) Adaptive attentional network for few-shot knowledge graph completion. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 1681–1691

  42. Baek J, Lee DB, Hwang SJ (2020) Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction. Adv Neural Inf Process Syst 33:546–560

    Google Scholar 

  43. Wang S, Huang X, Chen C, Wu L, Li J (2021) Reform: Error-aware few-shot knowledge graph completion. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 1979–1988

  44. Shi B, Weninger T (2018) Open-world knowledge graph completion. In: Proceedings of the AAAI conference on artificial intelligence volume 32

  45. Niu L, Fu C, Yang Q, Li Z, Chen Z, Liu Q, Zheng K (2021) Open-world knowledge graph completion with multiple interaction attention. World Wide Web 24:419–439

    Article  Google Scholar 

  46. Ye H, Zhang N, Deng S, Chen X, Chen H, Xiong F, Chen X, Chen H (2022) Ontology-enhanced prompt-tuning for few-shot learning. Proceedings of the ACM web conference 2022:778–787

    Google Scholar 

  47. Bosselut A, Rashkin H, Sap M, Malaviya C, Celikyilmaz A, Choi Y (2019) Comet: Commonsense transformers for knowledge graph construction. In: Association for Computational Linguistics (ACL)

  48. Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: Twenty-Fourth AAAI conference on artificial intelligence

  49. Vrandečić D, Krötzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78–85

    Article  Google Scholar 

  50. Sun Z, Deng Z-H, Nie J-Y, Tang J (2018) Rotate: Knowledge graph embedding by relational rotation in complex space. In: International conference on learning representations

  51. Kazemi SM, Poole D (2018) Simple embedding for link prediction in knowledge graphs. Adv Neural Inf Process Syst 31

  52. Yang B, Yih WT, He X, Gao J, Deng L (2015) Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the international conference on learning representations (ICLR) 2015

  53. Han X, Cao S, Lv X, Lin Y, Liu Z, Sun M, Li J (2018) Openke: An open toolkit for knowledge embedding. In: Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, pp 139–144

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62176236 and 62106225, Natural Science Foundation of Zhejiang Province under Grant LZ24F030011, Science and Technology Program of Zhejiang Province under Grant 2022C03113 and 2024C03274.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Xu-Hua Yang, Dong Wei, Lian Zhang, Gang-Feng Ma, Xin-Li Xu & Hai-Xia Long

Authors
  1. Xu-Hua Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gang-Feng Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xin-Li Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hai-Xia Long
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xu-Hua Yang.

Ethics declarations

Conflict of interest

All authors declare that have no conflict of interest.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, XH., Wei, D., Zhang, L. et al. Task-related network based on meta-learning for few-shot knowledge graph completion. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05480-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-05480-4

Keywords

Search

Navigation