Skip to main content
SpringerLink
Log in

Relation representation based on private and shared features for adaptive few-shot link prediction

  • Research
  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

Although Knowledge Graphs (KGs) provide great value in many applications, they are often incomplete with many missing facts. KG Completion (KGC) is a popular technique for knowledge supplement. However, there are two fundamental challenges for KGC. One challenge is that few entity pairs are often available for most relations, and the other is that there exists complex relations, including one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N). In this paper, we propose a new model to accomplish Few-shot KG Completion (FKGC) under complex relations, which is called Relation representation based on Private and Shared features for Adaptive few-shot link prediction (RPSA). In this model, we utilize the hierarchical attention mechanism for extracting the essential and crucial hidden information regarding the entity’s neighborhood so as to improve its representation. To enhance the representation of few-shot relations, we extract the private features (i.e., unique feature of each entity pair that represents the few-shot relation) and shared features (i.e., one or more commonalities among a few entity pairs that represent the few-shot relation). Specifically, a private feature extractor is used to extract the private semantic feature of the few-shot relation in the entity pair. After that, we design a shared feature extractor to extract the shared semantic features among a few reference entity pairs in the few-shot relation. Moreover, an adaptive aggregator aggregates several representations of the few-shot relation about the query. We conduct experiments on three datasets, including NELL-One, CoDEx-S-One and CoDEx-M-One datasets. According to the experimental results, the RPSA’s performance is better than that of the existing FKGC models. In addition, the RPSA model can also handle complex relations well, even in the few-shot scenario.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of Supporting Data

NELL-One and CoDEx are open-source datasets and can be downloaded from https://github.com/xwhan/One-shot-Relational-Learning and https://github.com/tsafavi/codex, respectively.

References

  • Bollacker, K., Evans, C., Paritosh, P., et al. (2008). Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, (pp. 1247–1250). https://doi.org/10.1145/1376616.1376746

  • Bordes, A., Usunier, N., Garcia-Duran, A., et al. (2013). Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, (pp. 2787–2795). https://doi.org/10.5555/2999792.2999923

  • Cai, L., Wang, L., Yuan, R., et al. (2023). Meta-learning based dynamic adaptive relation learning for few-shot knowledge graph completion. Big Data Research, 33(100), 394. https://doi.org/10.1016/J.BDR.2023.100394

    Article  Google Scholar 

  • Carlson, A., Betteridge, J., Kisiel, B., et al. (2010). Toward an architecture for never-ending language learning. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, (pp. 1306–1313). https://doi.org/10.1609/AAAI.V24I1.7519

  • Chen, M., Zhang, W., Zhang, W., et al. (2019). Meta relational learning for few-shot link prediction in knowledge graphs. In: Conference on Empirical Methods in Natural Language Processing, (pp. 4217–4226). https://doi.org/10.18653/V1/D19-1431

  • Dettmers, T., Minervini, P., Stenetorp, P., et al. (2018). Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI conference on artificial intelligence, (pp. 1811–1818). https://doi.org/10.1609/AAAI.V32I1.11573

  • Hao, Y., Liu, H., He, S., et al. (2018). Pattern-revising enhanced simple question answering over knowledge bases. In: Proceedings of the 27th International Conference on Computational Linguistics, (pp. 3272–3282). https://aclanthology.org/C18-1277/

  • Huang, X., Zhang, J., Li, D., et al. (2019). Knowledge graph embedding based question answering. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, (pp. 105–113). https://doi.org/10.1145/3289600.3290956

  • Huang, J., Lu, T., Zhu, J., et al. (2022). Multi-relational knowledge graph completion method with local information fusion. Applied Intelligence, 52, 7985–7994. https://doi.org/10.1007/S10489-021-02876-4

    Article  Google Scholar 

  • Hu, X., Duan, J., & Dang, D. (2021). Natural language question answering over knowledge graph: the marriage of sparql query and keyword search. Knowledge and Information Systems, 63(4), 819–844. https://doi.org/10.1007/S10115-020-01534-4

    Article  Google Scholar 

  • Hu, S., Zou, L., Yu, J. X., et al. (2018). Answering natural language questions by subgraph matching over knowledge graphs. IEEE Transactions on Knowledge and Data Engineering, 30(5), 824–837. https://doi.org/10.1109/TKDE.2017.2766634

    Article  Google Scholar 

  • Kipf, T.N., Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1609.02907

  • Lai, T., Cheng, L., Wang, D., et al. (2022). RMAN: relational multi-head attention neural network for joint extraction of entities and relations. Applied Intelligence, 52, 3132–3142. https://doi.org/10.1007/S10489-021-02600-2

    Article  Google Scholar 

  • Lample, G., Ballesteros, M., Subramanian, S., et al. (2016). Neural architectures for named entity recognition. In: Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (pp. 260–270). https://doi.org/10.18653/V1/N16-1030

  • Li, Z., Geng, P., Cao, S., et al. (2022). Few-shot knowledge graph completion based on data enhancement. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, IEEE, (pp. 1607–1611). https://doi.org/10.1109/BIBM55620.2022.9995024

  • Lin, Y., Liu, Z., Sun, M., et al. (2015). Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, (pp. 2181–2187). https://doi.org/10.1609/AAAI.V29I1.9491

  • Li, H., Wang, Y., Zhang, S., et al. (2021). KG4Vis: A knowledge graph-based approach for visualization recommendation. IEEE Transactions on Visualization and Computer Graphics, 28(1), 195–205. https://doi.org/10.1109/TVCG.2021.3114863

    Article  Google Scholar 

  • Lv, X., Gu, Y., Han, X., et al. (2019). Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, (pp. 3374–3379). https://doi.org/10.18653/V1/D19-1334

  • Ma, R., Han, X., Yan, L., et al. (2023). Modeling and querying temporal rdf knowledge graphs with relational databases. Journal of Intelligent Information Systems, 61, 569–609. https://doi.org/10.1007/S10844-023-00780-6

    Article  Google Scholar 

  • Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., et al. (2018). A novel embedding model for knowledge base completion based on convolutional neural network. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (pp. 327–333). https://doi.org/10.18653/V1/N18-2053

  • Nickel, M., Tresp, V., & Kriegel, H. (2011). A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning, (pp. 809–816). https://doi.org/10.5555/3104482.3104584

  • Peng, Z., Yu, H., & Jia, X. (2022). Path-based reasoning with k-nearest neighbor and position embedding for knowledge graph completion. Journal of Intelligent Information Systems, 58, 513–533. https://doi.org/10.1007/S10844-021-00671-8

    Article  Google Scholar 

  • Sacenti, J. A., Fileto, R., & Willrich, R. (2022). Knowledge graph summarization impacts on movie recommendations. Journal of Intelligent Information Systems, 58, 43–66. https://doi.org/10.1007/S10844-021-00650-Z

    Article  Google Scholar 

  • Safavi, T., & Koutra, D. (2020). CoDEx: A Comprehensive Knowledge Graph Completion Benchmark. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, (pp. 8328–8350). https://doi.org/10.18653/V1/2020.EMNLP-MAIN.669

  • Schlichtkrull, M.S., Kipf, T.N., Bloem, P., et al. (2018). Modeling relational data with graph convolutional networks. In: The Semantic Web, (pp. 593–607). https://doi.org/10.1007/978-3-319-93417-4_38

  • Sheng, J., Guo, S., Chen, Z., et al. (2020). Adaptive attentional network for few-shot knowledge graph completion. In: Conference on Empirical Methods in Natural Language Processing, (pp. 1681–1691). https://doi.org/10.1609/AAAI.V34I03.5698

  • Song, Q., Wu, Y., Lin, P., et al. (2018). Mining summaries for knowledge graph search. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1887–1900. https://doi.org/10.1109/TKDE.2018.2807442

    Article  Google Scholar 

  • Suchanek, F.M., Kasneci, G., & Weikum, G. (2007). Yago: a core of semantic knowledge. In: Proceedings of the 16th international conference on World Wide Web, (pp. 697–706). https://doi.org/10.1145/1242572.1242667

  • Trouillon, T., Welbl, J., Riedel, S., et al. (2016). Complex embeddings for simple link prediction. In: Proceedings of the 33nd International Conference on Machine Learning, PMLR, (pp. 2071–2080). https://doi.org/10.5555/3045390.3045609

  • Vrandecic, D., & Krtoetzsch, M. (2014). Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10), 78–85. https://doi.org/10.1145/2629489

    Article  Google Scholar 

  • Wang, X., He, X., Cao, Y., et al. (2019c). KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 950–958). https://doi.org/10.1145/3292500.3330989

  • Wang, F., Xie, Y., Zhang, K., et al. (2021). Bert-based knowledge graph completion algorithm for few-shot. In: Proceedings of the 2nd International Conference on Big Data Economy and Information Management, IEEE, (pp. 217–224). https://doi.org/10.1109/BDEIM55082.2021.00051

  • Wang, Z., Zhang, J., Feng, J., et al. (2014). Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, (pp. 1112–1119). https://doi.org/10.1609/AAAI.V28I1.8870

  • Wang, H., Zhang, F., Wang, J., et al. (2018a). Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, (pp. 417–426). https://doi.org/10.1145/3269206.3271739

  • Wang, H., Zhang, F., Xie, X., et al. (2018b). DKN: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, (pp. 1835–1844). https://doi.org/10.1145/3178876.3186175

  • Wang, H., Zhang, F., Zhang, M., et al. (2019b). Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 968–977). https://doi.org/10.1145/3292500.3330836

  • Wang, Q., Mao, Z., Wang, B., et al. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724–2743. https://doi.org/10.1109/TKDE.2017.2754499

    Article  Google Scholar 

  • Wang, H., Zhang, F., Wang, J., et al. (2019). Exploring high-order user preference on the knowledge graph for recommender systems. ACM Transactions on Information Systems, 37(3), 32:1-32:26. https://doi.org/10.1145/3312738

    Article  Google Scholar 

  • Xie, P., Zhou, G., Liu, J., et al. (2023). Incorporating global-local neighbors with gaussian mixture embedding for few-shot knowledge graph completion. Expert Systems with Applications, 234(121), 086. https://doi.org/10.1016/J.ESWA.2023.121086

    Article  Google Scholar 

  • Xiong, W., Mo, Y., Chang, S., et al. (2018). One-shot relational learning for knowledge graphs. In: Conference on Empirical Methods in Natural Language Processing, (pp. 1980–1990.) https://doi.org/10.18653/V1/D18-1223

  • Yang, C., & Zhang, W. (2022). Private and shared feature extractors based on hierarchical neighbor encoder for adaptive few-shot knowledge graph completion. In: Proceedings of IEEE 34th International Conference on Tools with Artificial Intelligence, (pp. 409–416). https://doi.org/10.1109/ICTAI56018.2022.00067

  • Yang, B., Yih, W., He, X., et al. (2015). Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the 3rd International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1412.6575

  • Yu, M., Jiang, T., Yu, J., et al. (2023). Sepake: a structure-enhanced and position-aware knowledge embedding framework for knowledge graph completion. Applied Intelligence, 53, 23,113-23,123. https://doi.org/10.1007/S10489-023-04723-0

    Article  Google Scholar 

  • Zhang, C., Yao, H., Huang, C., et al. (2020a). Few-shot knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, (pp. 3041–3048). https://doi.org/10.1609/AAAI.V34I03.5698

  • Zhang, Z., Zhuang, F., Zhu, H., et al. (2020b). Relational graph neural network with hierarchical attention for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, (pp. 9612–9619). https://doi.org/10.1609/AAAI.V34I05.6508

  • Zhang, H., Chen, Q., & Zhang, W. (2022). Improving entity linking with two adaptive features. Frontiers of Information Technology & Electronic Engineering, 23, 1620–1630. https://doi.org/10.1631/FITEE.2100495

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515011885 and 2023A1515011577, Guangzhou Science and Technology Planning Project under Grant 202201011835, International Science and Technology Cooperation Project in Huangpu District under Grant 2022GH08, Top Youth Talent Project of Zhujiang Talent Program under Grant 2019QN01X516, and Guangdong Provincial Key Laboratory of Cyber-Physical System under Grant 2020B1212060069.

Funding

Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515011885 and 2023A1515011577, Guangzhou Science and Technology Planning Project under Grant 202201011835, International Science and Technology Cooperation Project in Huangpu District under Grant 2022GH08, Top Youth Talent Project of Zhujiang Talent Program under Grant 2019QN01X516, and Guangdong Provincial Key Laboratory of Cyber-Physical System under Grant 2020B1212060069.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Weiwen Zhang & Canqun Yang

Authors
  1. Weiwen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Canqun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

W. Zhang contributed to the study conception, proposed the methodology and wrote the main manuscript text. C. Yang implemented the algorithms and conducted the experiments. All the authors edited and reviewed the manuscript.

Corresponding author

Correspondence to Weiwen Zhang.

Ethics declarations

Competing Interests

The authors declare no competing interests.

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

Zhang, W., Yang, C. Relation representation based on private and shared features for adaptive few-shot link prediction. J Intell Inf Syst (2024). https://doi.org/10.1007/s10844-024-00856-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10844-024-00856-x

Keywords

Search

Navigation