• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 11, Pages: 942-955

Original Article

Knowledge graph embedding via the properties of mapping and dynamic matrix

Received Date:02 December 2020, Accepted Date:25 February 2021, Published Date:09 April 2021


Objective: The main objective of this research is constructing a knowledge graph using a superior model that leverage the knowledge through relational mapping properties and the matrix constructed dynamically based on the changes in the relational mapping. Methods: This research work proposes symbolic objects in the knowledge graph that are signified by two vectors, the first vector denotes the entity/relation and the vector denote the matrix which is constructed with the help of mapping properties. The proposed methodology uses only minimum parameters and avoids multiplication processes which make the proposed scheme effective in the large scale graphs. The matrix representation with the assistance of mapping properties reduces the computational complication and the time requires to attain the knowledge is also minimized. Findings: The empirical research on knowledge graph mining gives significant insights and it is applied on benchmark datasets namely FB15K-237 and WN18RR. The performance are evaluated in terms of precision and recall in percentage.

Keywords: Knowledge embedding; Entity; Dynamic matrix; Relation and Edge


  1. Fu X, Ren X, Mengshoel OJ, Wu X. Stochastic optimization for market return prediction using financial knowledge graph. IEEE International Conference on Big Knowledge. 2018;p. 25–32.
  2. Rotmensch M, Halpern Y, Tlimat A, Horng S, Sontag D. Learning a health knowledge graph from electronic medical records. Scientific Reports. 2017;7(1):1–11. Available from: https://dx.doi.org/10.1038/s41598-017-05778-z
  3. Nathani D, Chauhan J, Sharma C, Kaul M. Learning attention-based embeddings for relation prediction in knowledge graphs. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019;p. 4710–4723.
  4. Li C, Xian X, Ai X, Cui Z. Representation Learning of Knowledge Graphs with Embedding Subspaces. Scientific Programming. 2020;2020:1–10. Available from: https://dx.doi.org/10.1155/2020/4741963
  5. Bin C, Qin S, Rao G, Gu T, Chang L. Multiview Translation Learning for Knowledge Graph Embedding. Scientific Programming. 2020;2020:1–9. doi: 10.1155/2020/7084958
  6. Tan Z, Zhao X, Fang Y, Xiao W, Tang J. Knowledge representation learning via dynamic relation spaces. p. 684–691.
  7. Zhu JZ, Jia YT, Xu J. Modeling the Correlations of Relations for Knowledge Graph Embedding. J Comput Sci Technol. 2018;33:323–334.
  8. Tan C, Yang K, Dai X, Huang S, Chen J. MSGE: A Multi-step Gated Model for Knowledge Graph Completion. Singapore. 2020:424–435.
  9. Chang L, Zhu M, Gu T, Bin C, Qian J, Zhang J. Knowledge Graph Embedding by Dynamic Translation. IEEE Access. 2017;5:20898–20907. Available from: https://dx.doi.org/10.1109/access.2017.2759139
  10. Xie R, Liu Z, Jia J, Luan H, Sun M. Representation learning of knowledge graphs with entity descriptions. p. 2659–2665.
  11. Chen Z, Wang Y, Zhao B, Cheng J, Zhao X, &duan Z. Knowledge Graph Completion: A Review. IEEE Access. 2020;p. 1.
  12. Celebi R, Uyar H, Yasar E, Gumus O, Dikenelli O, Dumontier M. Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings. BMC Bioinformatics. 2019;20(1):726. Available from: https://dx.doi.org/10.1186/s12859-019-3284-5
  13. Zhao F, Sun H, Jin L, Jin H. Structure-augmented knowledge graph embedding for sparse data with rule learning. Computer Communications. 2020;159:271–278. Available from: https://dx.doi.org/10.1016/j.comcom.2020.05.017
  14. Sousa RT, Silva S, Pesquita C. Evolving knowledge graph similarity for supervised learning in complex biomedical domains. BMC Bioinformatics. 2020;21(1):6. Available from: https://dx.doi.org/10.1186/s12859-019-3296-1
  15. Trouillon T, Wealbl J, Riedel S, Gaussier É, Bouchard G. Complex embeddings for simple link prediction. In: Proceedings of the 33rd International Conference on Machine Learning. (Vol. 48, pp. 2071-2080) 2016.
  16. Hu X, Duan J, Dang D. Scalable aggregate keyword query over knowledge graph. Future Generation Computer Systems. 2020;107:588–600. Available from: https://dx.doi.org/10.1016/j.future.2020.02.011
  17. Ji G, Liu K, He S, Zhao J. Knowledge graph completion with adaptive sparse transfer matrix. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. 2016;p. 985–991.


© 2021 Sangeetha Devi & Kalaivani.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).


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