Skip to main content
SpringerLink
Log in

Knowledge graph embedding by projection and rotation on hyperplanes for link prediction

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Knowledge is increasingly completed due to connections formed in a knowledge graph, enabling a complete understanding of reality. Link prediction plays an important role in this process. Among the multiple methods that exist to tackle this problem, the geometry-based prediction method has attracted attention due to its intuitiveness and capacity to flexibly address various types of relations. We propose the rotation embedding of entities on separate relation-specific hyperplanes as an alternative to the translation method. Moreover, instead of optimizing the model by tight constraints, we add several soft constraints to minimize the loss function. Experimenting on standard datasets with numerous evaluation metrics, the proposed model outperforms both state-of-the-art and baseline models. We also analyze the model on multiple batch sizes and negative sample size values, along with various embedding dimensions and optimizers. Thereby, we demonstrate the impact of the parameters on the geometry-based link prediction model and provide a basis for future improvement.

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
Algorithm 1
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. https://github.com/lnthanhhcmus/RotatPRH

References

  1. Berners-Lee T, Chen Y, Chilton L et al (2006) Tabulator: exploring and analyzing linked data on the semantic web. In: Proceedings of the 3rd international semantic web user interaction workshop (SWUI) at ISWC, Athens, Georgia

  2. Auer S, Bizer C, Kobilarov G et al (2007) DBpedia: a nucleus for a web of open data. In: Dbpedia: a nucleus for a web of open data. The semantic web. Springer, pp 722–735

    Google Scholar 

  3. Ahlers D (2013) Assessment of the accuracy of GeoNames gazetteer data. In: Proceedings of the 7th workshop on geographic information retrieval, GIR'13. ACM, New York, pp 74–81

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  5. Amit S (2012) Google knowledge graph. Google Product Blog https://blog.google/products/search/introducing-knowledgegraph-things-not/

  6. Schneider EW (1973) Course modularization applied: the interface system and its implications for sequence control and data analysis. In: Association for the development of instructional systems (ADIS), Chicago

  7. Noy N, Gao Y, Jain A, Narayanan A, Patterson A, Taylor J (2019) Industry-scale knowledge graphs: lessons and challenges. Commun ACM 62:36–43. https://doi.org/10.1145/3331166

    Article  Google Scholar 

  8. Ji S, Pan S, Cambria E et al (2021) A survey on knowledge graphs: representation, acquisition and applications. IEEE Trans Neural Netw Learning Syst 33:494–514. https://doi.org/10.1109/TNNLS.2021.3070843

    Article  MathSciNet  Google Scholar 

  9. Berlusconi G, Calderoni F, Parolini N, Verani M, Piccardi C (2016) Link prediction in criminal networks: a tool for criminal intelligence analysis. PLoS One 11:e0154244. https://doi.org/10.1371/journal.pone.0154244

    Article  Google Scholar 

  10. Oniani D, Jiang G, Liu H, Shen F (2020) Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases. J Am Med Inform Assoc 27:1259–1267. https://doi.org/10.1093/jamia/ocaa117

    Article  Google Scholar 

  11. Shi B, Weninger T (2018) Open-world knowledge graph completion. In: Proceedings of the 32th AAAI conference on artificial intelligence, pp 1957–1964. https://doi.org/10.1609/aaai.v32i1.11535

    Chapter  Google Scholar 

  12. Hogan A, Blomqvist E, Cochez M et al (2022) Knowledge graphs. ACM Comput Surv 71:1–37. https://doi.org/10.1145/3447772

    Article  Google Scholar 

  13. Lu L, Zhou T (2011) Link prediction in complex networks: a survey. Physica A: Statistical Mechanics and its Applications 390:1150–1170. https://doi.org/10.1016/j.physa.2010.11.027

    Article  Google Scholar 

  14. Meilicke C, Chekol MW, Ruffinelli D, Stuckenschmidt H (2019) Anytime bottom-up rule learning for knowledge graph completion. In: Proceedings of the 28th international joint conference on artificial intelligence. IJCAI-19, pp 3137–3143. https://doi.org/10.24963/ijcai.2019/435

    Chapter  Google Scholar 

  15. Qu M, Tang J (2019) Probabilistic logic neural networks for reasoning. International Conference on Neural Information Processing Systems 693:7712–7722

    Google Scholar 

  16. Nayyeri M, Xu C, Lehmann J, Yazdi HS (2021) LogicENN: a neural based knowledge graphs embedding model with logical rules. IEEE Trans Pattern Anal Mach Intell:1. https://doi.org/10.1109/TPAMI.2021.3121646

  17. Bordes A, Usunier N, Garcia-Duran A et al (2013) Translating embeddings for modeling multi-relational data. Adv Neural Inf Proces Syst 26:2787–2795

    Google Scholar 

  18. Nguyen DQ, Sirts K, Qu L, Johnson M (2016) STransE: a novel embedding model of entities and relationships in knowledge bases. In: Proceedings of the 15th conference of the north american chapter of the association for computational linguistics: human language technologies (NAACL-HLT'16), San Diego, pp 460–466. https://doi.org/10.18653/v1/N16-1054

  19. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the 28th AAAI conference on artificial intelligence, Québec, pp 1112–1119. https://doi.org/10.1609/aaai.v28i1.8870

  20. Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30:1616–1637. https://doi.org/10.1109/TKDE.2018.2807452

    Article  Google Scholar 

  21. Yang B, Yih W, He X et al (2015) Embedding entities and relations for learning and inference in knowledge bases, Proceedings of the 3rd international conference on learning representations. ICLR 2015, San Diego, pp 1–13

  22. Kazemi SM, Poole D (2018) Simple embedding for link prediction in knowledge graphs, Proceedings of the 32nd international conference on neural information processing systems. NeurIPS, Montréal, pp 4289–4300

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

    Google Scholar 

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

    Google Scholar 

  25. Nguyen DQ, Nguyen TD, Nguyen DQ, Phung D (2018) A novel embedding model for knowledge base completion based on convolutional neural network, Proceedings of the 16th conference of the north american chapter of the association for computational linguistics: human language technologies, New Orleans, pp 327–333. https://doi.org/10.18653/v1/N18-2053

  26. Guo L, Sun Z, Hu W (2019) Learning to exploit long-term relational dependencies in knowledge graphs. In: International conference on machine learning, pp 2505–2514

    Google Scholar 

  27. Sun Z, Deng Z-H, Nie J-Y, Tang J (2019) Rotate: knowledge graph embedding by relational rotation in complex space. In: International conference on learning representations

    Google Scholar 

  28. Bordes A, Weston J, Collobert R, Bengio Y (2011) Learning structured embeddings of knowledge bases. In: AAAI conference on artificial intelligence, pp 301–306

    Google Scholar 

  29. Zhang W, Paudel B, Zhang W et al (2019) Interaction embeddings for prediction and explanation in knowledge graphs. In: ACM international conference on web search and data mining, pp 96–104

    Google Scholar 

  30. Lin Y, Liu Z, Sun M et al (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI conference on artificial intelligence, Austin, pp 2181–2187. https://doi.org/10.1609/aaai.v29i1.9491

  31. Ebisu T, Ichise R (2018) TorusE: knowledge graph embedding on a lie group. In: AAAI conference on artificial intelligence

    Google Scholar 

  32. Bouchard G, Singh S, Trouillon T (2015) On approximate reasoning capabilities of low-rank vector spaces. In: AAAI spring symposium on knowledge representation and reasoning (KRR): integrating symbolic and neural approaches. AAAI Press, Palo Alto, pp 6–9

    Google Scholar 

  33. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: International Conference on Learning Representations

  34. Bollacker K, Evans C, Paritosh P et al (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: ACM SIGMOD international conference on management of data, pp 1247–1250

    Google Scholar 

  35. Toutanova K, Chen D (2015) Observed versus latent features for knowledge base and text inference. In: Workshop on continuous vector space models and their compositionality, pp 57–66

    Chapter  Google Scholar 

  36. Miller GA (1995) WordNet: a lexical database for English. Commun ACM 38:39–41. https://doi.org/10.1145/219717.219748

    Article  Google Scholar 

  37. Mahdisoltani F, Biega J, Suchanek F (2014) YAGO3: a knowledge base from multilingual wikipedias. In: Conference on innovative data systems research

    Google Scholar 

  38. Chen Z, Wang Y, Zhao B, Cheng J, Zhao X, Duan Z (2020) Knowledge graph completion: a review. IEEE Access 8:192435–192456. https://doi.org/10.1109/ACCESS.2020.3030076

    Article  Google Scholar 

  39. Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159

    MathSciNet  MATH  Google Scholar 

  40. Trouillon T, Welbl J, Riedel S et al (2016) Complex embeddings for simple link prediction, Proceedings of the 33rd international conference on machine learning. ICML'16, New York, pp 2071–2080

Download references

Acknowledgments

This work is supported by the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam under grant number CNTT 2022-02.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam

    Thanh Le, Ngoc Huynh & Bac Le

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Thanh Le, Ngoc Huynh & Bac Le

Authors
  1. Thanh Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ngoc Huynh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bac Le
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bac Le.

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 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

Le, T., Huynh, N. & Le, B. Knowledge graph embedding by projection and rotation on hyperplanes for link prediction. Appl Intell 53, 10340–10364 (2023). https://doi.org/10.1007/s10489-022-03983-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03983-6

Keywords

Search

Navigation