Dynamic dual quaternion knowledge graph embedding | Applied Intelligence Skip to main content
Springer Nature Link
Log in

Dynamic dual quaternion knowledge graph embedding

  • Published:
Applied Intelligence Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Knowledge graph aims to describe the concepts, the entities and the complex relations of them in the real world. Recently, a series of quaternion rotation models that usually considering the relation vector as the rotation between head and tail entities, have been extensively studied. The results showed these models had the advantages of simplicity and efficiency. However, they are quite weak in capturing the representation and the feature interaction between entities and relations, resulting in insufficient expressiveness of the underlying models, because these models only focus on the linear relations between entities and relations. In order to solve this problem, this paper proposes a novel knowledge graph embedding model called DualDE, which dynamically maps the dual quaternions to the knowledge graph. Specifically, DualDE uses a dynamic mapping mechanism to construct the entity transition vector and the relation transition vector, and continuously adjusts the embedding position of the entity vector in the dual quaternion space according to the dual quaternion multiplication rules. In addition, DualDE can dynamically construct a variety of complex relation types, such as one-to-many, many-to-one and many-to-many. The experimental results based on three standard data sets show that the DualDE model is superior to the existing knowledge graph embedding models on many metrics.

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
¥17,985 /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 includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

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

References

  1. Zhang F, Yuan NJ, Lian D, Xie X, Ma WY (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 353–362

  2. Hao Y, Zhang Y, Liu K, He S, Liu Z, Wu H, Zhao J (2017) An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 221–231

  3. Xiong C, Power R, Callan J (2017) Explicit semantic ranking for academic search via knowledge graph embedding. In: Proceedings of the 26th International Conference on World Wide Web, pp 1271–1279

  4. Bansal T, Juan DC, Ravi S, McCallum A (2019) A2n: Attending to neighbors for knowledge graph inference. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp 4387–4392

  5. Fabian MS, Gjergji K, Gerhard WEIKUM (2007) Yago: A core of semantic knowledge unifying wordnet and wikipedia. In: 16th International World Wide Web Conference, WWW, pp 697–706

  6. 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

  7. Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Bizer C (2015) Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web 6(2):167–195

    Article  Google Scholar 

  8. Wang H, Jiang S, Yu Z (2020) Modeling of complex internal logic for knowledge base completion. Appl Intell 50:3336–3349

    Article  Google Scholar 

  9. Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: A survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724–2743

    Article  Google Scholar 

  10. Fang Y, Wang H, Zhao L, Yu F, Wang C (2020) Dynamic knowledge graph based fake-review detection. Appl Intell 50(12):4281–4295

    Article  Google Scholar 

  11. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Neural Information Processing Systems (NIPS), pp 2787–2795

  12. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence 28(1):1112–1119

    Article  Google Scholar 

  13. 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(1):2181–2181

    Article  Google Scholar 

  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. Yang B, Yih WT, He X, Gao J, Deng L (2014) Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575

  16. Trouillon T, Welbl J, Riedel S, Gaussier Ã, Bouchard G (2016) Complex embeddings for simple link prediction. In: International Conference on Machine Learning, PMLR, pp 2071–2080

  17. Dettmers T, Minervini P, Stenetorp P, Riedel S (2018) Convolutional 2d knowledge graph embeddings. Proceedings of the AAAI Conference on Artificial Intelligence 32(1):1811–1818

    Article  Google Scholar 

  18. Nguyen DQ, Nguyen TD, Nguyen DQ, Phung D (2017) A novel embedding model for knowledge base completion based on convolutional neural network. arXiv preprint arXiv:1712.02121

  19. Vu T, Nguyen TD, Nguyen DQ, Phung D (2019) A capsule network-based embedding model for knowledge graph completion and search personalization. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 2180–2189

  20. Sun Z, Deng ZH, Nie JY, Tang J (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197

  21. Zhang S, Tay Y, Yao L, Liu Q (2019) Quaternion knowledge graph embeddings. arXiv preprint arXiv:1904.10281

  22. Chen H, Wang W, Li G, Shi Y (2020) A quaternion-embedded capsule network model for knowledge graph completion. IEEE Access 8:100890–100904

    Article  Google Scholar 

  23. Gao C, Sun C, Shan L, Lin L, Wang M (2020) Rotate3D: Representing relations as rotations in three-dimensional space for knowledge graph embedding. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 385–394

  24. Cao Z, Xu Q, Yang Z, Cao X, Huang Q (2021) Dual Quaternion knowledge graph embeddings. Proceedings of the AAAI Conference on Artificial Intelligence 35(8):6894–6902

    Article  Google Scholar 

  25. Gao H, Yang K, Yang Y, Zakari RY, Owusu JW, Qin K (2021) QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion. arXiv preprint arXiv:2105.09002

  26. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546

  27. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  28. Mikolov T, Yih WT, Zweig G (2013) Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 746–751

  29. Lin Y, Liu Z, Sun M (2016) Knowledge representation learning with entities, attributes and relations. Ethnicity 1:41–52

    Google Scholar 

  30. Hamilton WR (1844) On quaternions; or on a new system of imaginaries in algebra. Philosophical Magazine 25(3):489–495

    Google Scholar 

  31. Clifford MA (1871) Preliminary sketch of biquaternions. Proceedings of the London Mathematical Society 1(1):381–395

    Article  MathSciNet  Google Scholar 

  32. Toutanova K, Chen D (2015) Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality, pp 57–66

  33. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lerer A (2017). Automatic differentiation in pytorch

  34. Nickel M, Rosasco L, Poggio T (2016) Holographic embeddings of knowledge graphs. Proceedings of the AAAI Conference on Artificial Intelligence 30(1):1955–1961

    Article  Google Scholar 

  35. Jia N, Cheng X, Su S (2020) Improving knowledge graph embedding using locally and globally attentive relation paths. European Conference on Information Retrieval. Springer, Cham, pp 17–32

    Google Scholar 

  36. Dou J, Tian B, Zhang Y, Xing C (2021) A novel embedding model for knowledge graph completion based on multi-task learning. International Conference on Database Systems for Advanced Applications. Springer, Cham, pp 240–255

    Chapter  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61976032 and No.61806038), the Scientific Research Funding Project of Education Department of Liaoning Province (No.2020JYT03) and the Innovative Talents in Colleges and Universities of Liaoning Province (No.WR2019005).

Author information

Authors and Affiliations

  1. Faculty of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Heng Chen, Guanyu Li, Wei Jiang & Yunhao Sun

  2. Research Center for Language Intelligence, Dalian University of Foreign Languages, Dalian, 116044, China

    Heng Chen

Authors
  1. Heng Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Guanyu Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wei Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yunhao Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Guanyu Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, H., Li, G., Jiang, W. et al. Dynamic dual quaternion knowledge graph embedding. Appl Intell 52, 14153–14163 (2022). https://doi.org/10.1007/s10489-021-03069-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-03069-9

Keywords