Skip to main content
Springer Nature Link
Log in

RIECN: learning relation-based interactive embedding convolutional network for knowledge graph

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Most knowledge graphs(KGs) are large and incomplete graph-structure database, which can be completed by predicting miss links according to the existing knowledge. The mainstream method is knowledge graph embedding (KGE) which is designed to learn low dimensional embedding of entities and relations. However, knowledge graph embedding still faces two major issues: (1) How to generate more expressive embeddings? (2) How to solve semantic polysemy of entities in different relations? In this paper, we propose a novel KG embedding model, RIECN (Relation-based Interactive Embedding Convolutional Network), which achieves high-quality performance and shows some advancements in modeling complex relations. In RIECN, FIR (Feature Interaction Reshaping) method is introduced to increase the feature interactions between entity and relation embeddings to generate more expressive feature maps. In addition, a new method of generating relation-based dynamic convolution filters, RDCF, is proposed. RDCF generates specific relation and hybird-size convolution filters, which enriches the feature maps of each entity improving the accuracy of link prediction task especially in complex relations scenario. We tested the performance of our model on five benchmark datasets. The experimental results show that the RIECN model significantly outperforms recent state-of-the-art models by 0.1–3.2% and 1.1–3.7%, in terms of MMR metric and Hit@1 metric, respectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The datasets generated during and/or analysed during the current study are available in the OpenKE repository, [https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch/benchmarks].

Notes

  1. https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch/benchmarks.

References

  1. Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (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

  2. Miller GA (1995) Wordnet: A lexical database for English. Commun ACM 38(11):39–41

    Article  Google Scholar 

  3. Suchanek FM, 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

  4. Gao L, Cao L, Xu X, Shao J, Song J (2020) Question-led object attention for visual question answering. Neurocomputing 391:227–233

    Article  Google Scholar 

  5. Hao Y, Liu H, He S, Liu K, Zhao J (2018) Pattern-revising enhanced simple question answering over knowledge bases. In: Proceedings of the 27th international conference on computational linguistics. pp 3272–3282

  6. Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2019) Exploring high-order user preference on the knowledge graph for recommender systems. ACM Trans Inf Syst 37(3)

  7. Cao Y, Wang X, He X, Hu Z, Chua T-S (2019) Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In: The world wide web conference. pp 151–161

  8. Wang H, Zhang F, Zhao M, Li W, Xie X, Guo M (2019) Multi-task feature learning for knowledge graph enhanced recommendation. In: The world wide web conference. pp 2000–2010

  9. Zhou H, Young T, Huang M, Zhao H, Xu J, Zhu X (2018) Commonsense knowledge aware conversation generation with graph attention. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18. pp 4623–4629

  10. Wang M, Qiu L, Wang X (2021) A survey on knowledge graph embeddings for link prediction. Symmetry 13(3):485

    Article  Google Scholar 

  11. Rossi A, Barbosa D, Firmani D, Matinata A, Merialdo P (2021) Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans Knowl Discov from Data (TKDD) 15(2):1–49

    Article  Google Scholar 

  12. Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th international conference on neural information processing systems, vol 2. pp 2787–2795

  13. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Twenty-eighth AAAI conference on artificial intelligence. pp 1113–1119

  14. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. Proceedings of the AAAI conference on artificial intelligence 29:2181–2187

    Article  Google Scholar 

  15. 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

  16. Nickel M, Tresp V, Kriegel H-P, A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on International Conference on Machine Learning, (2011). pp 809–816

  17. Yang B, Yih, SW-t 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

  18. Trouillon T, Welbl J, Riedel S, Gaussier E, Bouchard G (2016) Complex embeddings for simple link prediction. In: Proceedings of the 33rd international conference on international conference on machine learning, vol 48. pp 2071–2080

  19. Nickel M, Rosasco L, Poggio T (2016) Holographic embeddings of knowledge graphs. Proceedings of the AAAI conference on artificial intelligence 30:1955–1961

    Article  Google Scholar 

  20. Liu H, Wu Y, Yang Y (2017) Analogical inference for multi-relational embeddings. In: International conference on machine learning. pp 2168–2178

  21. Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference. pp 593–607

  22. Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. In: 32nd AAAI conference on artificial intelligence. pp 1811–1818

  23. Zhang W, Paudel B, Zhang W, Bernstein A, Chen H (2019) Interaction embeddings for prediction and explanation in knowledge graphs. In: Proceedings of the Twelfth ACM international conference on web search and data mining. pp 96–104

  24. Li Z, Liu H, Zhang Z, Liu T, Shu J (2021) Recalibration convolutional networks for learning interaction knowledge graph embedding. Neurocomputing 427:118–130

    Article  Google Scholar 

  25. Li W, Peng R, Li Z (2022) Improving knowledge graph completion via increasing embedding interactions. Appl Intell. https://doi.org/10.1007/s10489-021-02947-6

    Article  Google Scholar 

  26. Shen Y, Li H, Li D, Zheng J, Wang W (2022) Angraph: attribute-interactive neighborhood-aggregative graph representation learning. Neural Comput Appl 1–13

  27. Ebisu T, Ichise R (2018) Toruse: knowledge graph embedding on a lie group. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

  28. Sun Z, Deng Z-H, Nie J-Y, Tang J (2019) Rotate: knowledge graph embedding by relational rotation in complex space, arXiv preprint arXiv:1902.10197

  29. Chairatanakul N, Liu X, Hoang NT, Murata T (2022) Heterogeneous graph embedding with single-level aggregation and infomax encoding. Mach Learn pp 1–30

  30. Wang S, Wei X, Nogueira dos Santos CN, Wang Z, Nallapati R, Arnold A, Xiang B, Yu PS, Cruz IF (2021) Mixed-curvature multi-relational graph neural network for knowledge graph completion. Proceedings of the web conference 2021:1761–1771

    Google Scholar 

  31. Shen Y, Li D, Nan D (2022) Modeling path information for knowledge graph completion. Neural Comput Appl 34(3):1951–1961

    Article  Google Scholar 

  32. Chen L, Cui J, Tang X, Qian Y, Li Y, Zhang Y (2022) Rlpath: a knowledge graph link prediction method using reinforcement learning based attentive relation path searching and representation learning. Appl Intell 52(4):4715–4726

    Article  Google Scholar 

  33. Liang X, Ma Y, Cheng G, Fan C, Yang Y, Liu Z (2022) Meta-path-based heterogeneous graph neural networks in academic network. Int J Mach Learn Cybern 13(6):1553–1569

    Article  Google Scholar 

  34. Lin X, Liang Y, Giunchiglia F, Feng X, Guan R (2019) Relation path embedding in knowledge graphs. Neural Comput Appl 31(9):5629–5639

    Article  Google Scholar 

  35. Wang B, Shen T, Long G, Zhou T, Wang Y, Chang Y (2021) Structure-augmented text representation learning for efficient knowledge graph completion. Proceedings of the web conference 2021:1737–1748

    Google Scholar 

  36. Ke P, Ji H, Ran Y, Cui X, Wang L, Song L, Zhu X, Huang M, Jointgt: Graph-text joint representation learning for text generation from knowledge graphs, arXiv preprint arXiv:2106.10502

  37. Ji S, Pan S, Cambria E, Marttinen P, Philip SY (2021) A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494–514

    Article  MathSciNet  Google Scholar 

  38. Liu Y, Hildebrandt M, Joblin M, Ringsquandl M, Raissouni R, Tresp V, Neural multi-hop reasoning with logical rules on biomedical knowledge graphs. In: European Semantic Web Conference, Springer, (2021). pp 375–391

  39. Xie Z, Zhou G, Liu J, Huang JX (2020) ReInceptionE: relation-aware inception network with joint local-global structural information for knowledge graph embedding. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 5929–5939

  40. Wei X, Zhang Y, Wang H (2022) Joint semantic embedding with structural knowledge and entity description for knowledge representation learning. Neural Comput Appl 1–20

  41. Nguyen DQ, Nguyen TD, Nguyen DQ, Phung D (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, vol 2 (short papers), pp 327–333

  42. Balažević I, Allen C, Hospedales TM, Hypernetwork knowledge graph embeddings. In: International conference on artificial neural networks, (2019), pp 553–565

  43. Zhang Z, Li Z, Liu H, Xiong NN (2020) Multi-scale dynamic convolutional network for knowledge graph embedding. IEEE Trans Knowl Data Eng 54:2335–2347

    Google Scholar 

  44. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  45. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning pp 448–456

  46. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  47. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 2818–2826

  48. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980

  49. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China under 62277028, Central Universities of CCNU under Grant CCNU19ZN013 and Key Technologies R\({ \& }\)D Program of China Xinjiang Production and Construction Corps of data-driven regional intelligent education service (2021AB023).

Author information

Authors and Affiliations

  1. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, 430079, China

    Wei Wang, Xiaoxuan Shen, Huanyu Zhang, Zhifei Li & Baolin Yi

Authors
  1. Wei Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xiaoxuan Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Huanyu Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhifei Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Baolin Yi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Baolin Yi.

Ethics declarations

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

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

Wang, W., Shen, X., Zhang, H. et al. RIECN: learning relation-based interactive embedding convolutional network for knowledge graph. Neural Comput & Applic 35, 8343–8356 (2023). https://doi.org/10.1007/s00521-022-08109-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08109-0

Keywords