Research on the Knowledge Representation Method of Power News Text Based on Time Hyperplane

Yun Wu; Ziyi Wang; Yan Du; Jieming Yang; Xinru Liu; Zhenhong Liu; Kai Yang

doi:10.20965/jaciii.2025.p1369

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JACIII Vol.29 No.6 pp. 1369-1376

doi: 10.20965/jaciii.2025.p1369

(2025)

Research Paper:

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Research on the Knowledge Representation Method of Power News Text Based on Time Hyperplane

Yun Wu^, Ziyi Wang^, Yan Du^**, Jieming Yang^*, Xinru Liu^***, Zhenhong Liu^,†, and Kai Yang^

^*School of Computer Science, Northeast Electric Power University
No.169 Changchun Road, Chuanying District, Jilin, Jilin 132012, China

^†Corresponding author

^**Jilin Meteorological Observation and Protection Center, Jilin Meteorological Service
No.176 Suizhong Road, Lvyuan District, Changchun City, Jilin 130000, China

^***Shandong University of Finance and Economics
40 Shungeng Road, Shizhong District, Jinan, Shandong 250014, China

Received:

January 28, 2025

Accepted:

July 7, 2025

Published:

November 20, 2025

Keywords:

knowledge representation, translation model, knowledge graph, triplet, hyperplane

Abstract

To address the problem that static knowledge graphs cannot evolve over time, which leads to the conflict between entities and relations in the process of knowledge representation, this paper combines the temporal hyperplane with the translation model in knowledge representation, and proposes a knowledge representation method based on the temporal hyperplane for power news texts. First, multiple temporal hyperplanes are established and the temporal factor is added to the scoring function of the translation model; then, the entities and relation of the power news are projected onto the temporal hyperplanes, and the optimal knowledge representation is determined according to the loss function. Taking the power news text as an example, this algorithm well resolves the time-related conflicts in the power news text, and the comprehensive indexes are significantly improved on the time-related triplets.

Cite this article as:

Y. Wu, Z. Wang, Y. Du, J. Yang, X. Liu, Z. Liu, and K. Yang, “Research on the Knowledge Representation Method of Power News Text Based on Time Hyperplane,” J. Adv. Comput. Intell. Intell. Inform., Vol.29 No.6, pp. 1369-1376, 2025.

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References

[1] Y. P. Sheng, “Research and Application of Learning Methods for Knowledge Graphs,” University of Electronic Science and Technology of China, Ph.D. Dissertation, 2020 (in Chinese). https://www.doi.org/10.27005/d.cnki.gdzku.2020.004504.
[2] S. Sen, “Research on Temporal Knowledge Graph Completion Algorithm Based on Knowledge Representation Learning,” University of Electronic Science and Technology of China, M.A. Thesis, 2022 (in Chinese). https://www.doi.org/10.27005/d.cnki.gdzku.2022.001827
[3] L. K. Zhou, Y. Yang, X. Ren, F. Wu, and Y. T. Zhang, “Dynamic Network Embedding by Modeling Triadic Closure Process,” Thirty-Second AAAI Conf. on Artificial Intelligence, Vol.32, No.1, 2018. https://doi.org/10.1609/aaai.v32i1.11257
[4] S. D. Winter, T. Decuypere, S. Mitrović, B. Baesens, and J. D. Weerdt, “Combining Temporal Aspects of Dynamic Networks with Node2Vec for a More Efficient Dynamic Link Prediction,” 2018 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining, pp. 1234-1241, 2018. https://doi.org/10.1109/ASONAM.2018.8508272
[5] Z. Y. Qu and P. F. Sun, “A Method of Knowledge Representation in Power Grid Domain Based on OWL,” J. of Northeast Dianli University, Vol.32, No.4, pp. 30-34, 2012 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2012.04.008
[6] P. Zhou, “Embedding model-based Research on Knowledge Graph Completion Method,” Xidian University, M.A. Thesis, 2020 (in Chinese). https://doi.org/10.27389/d.cnki.gxadu.2020.001326
[7] D. Y. Li, “Research on Knowledge Graph Completion Technology Based on Representation Learning,” Zhengzhou University of Light Industry, M.A. Thesis, 2022 (in Chinese). https://doi.org/10.27469/d.cnki.gzzqc.2022.000028
[8] C. Y. Sun, “The Research and Application of Entity and Relation Completion Based on Knowledge Graph,” University of Electronic Science and Technology of China, M.A. Thesis, 2022 (in Chinese). https://doi.org/10.27005/d.cnki.gdzku.2022.002303
[9] Z. W. Li, ”Knowledge Graph Completion Model Based On Importance Priority,” Harbin Institute of Technology, M.A. Thesis, 2020 (in Chinese). https://doi.org/0.27061/d.cnki.ghgdu.2021.000635
[10] M. Nayyeri, S. Vahdati, J. Lehmann, and H. S. Yazdi, “Soft Marginal TransE for Scholarly Knowledge Graph Completion,” arXiv:1904.12211, 2019. https://doi.org/10.48550/arXiv.1904.12211
[11] Z. Y. Qu, X. D. Fan, H. T. Yu, and N. Qu, “Smart Grid Text Knowledge Acquisition Model Based on Ontology,” J. Of Northeast Dianli University, Vol.34, No.5, pp, 60-68, 2014 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2014.05.012
[12] X. L. Guo and X. Han, “Grid Knowledge Collaborative Discovery Strategy Research,” J. of Northeast Dianli University, Vol.34, No.1, pp. 94-98, 2014 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2014.01.019
[13] Y. H. Wang, F. Yang, and S. Chen, “Power Virtual Communities-oriented Collection Methods of Tacit Knowledge,” J. of Northeast Dianli University, Vol.34, No.4, pp. 69-74, 2014 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2014.04.014
[14] A. Bordes, N. Usunier, A. G. Durán, J. Weston, and O. Yakhnenko, “Translating Embeddings for Modeling Multi-relational Data,” Neural Information Processing Systems, pp. 2787-2795, 2013. https://api.semanticscholar.org/CorpusID:14941970
[15] Z. Wang, J. W. Zhang, J. L. Feng, and Z. Chen, “Knowledge Graph Embedding by Translating on Hyperplanes,” AAAI Conf. on Artificial Intelligence, pp. 1112-1119, 2014. https://doi.org/10.1609/aaai.v28i1.8870
[16] T. S. Jiang, T. Y. Liu, T. Ge, L. Sha, B. B. Chang, S. J. Li, and Z. F. Sui, “Towards Time-Aware Knowledge Graph Completion,” Proc. of COLING 2016, the 26th Int. Conf. on Computational Linguistics: Technical Papers, pp. 1715-1724. https://aclanthology.org/C16-1161/
[17] J. Leblay and M. W. Chekol, “Deriving Validity Time in Knowledge Graph,” Companion of the Web Conf., pp. 1771-1776, 2018. https://dl.acm.org/doi/pdf/10.1145/3184558.3191639
[18] S. S. Dasgupta, S. N. Ray, and P. Talukdar, “HyTE: Hyperplane-based Temporally Aware Knowledge Graph Embedding,” Proc. of the 2018 Conf. on Empirical Methods in Natural Language Processing, pp. 2001-2011,2018. https://doi.org/10.18653/v1/D18-1225
[19] A. Paccanaro and G. Hinton, “Learning Distributed Representations of Concepts using Linear Relational Embedding,” IEEE Trans. on Knowledge and Data Engineering, Vol.13, No.2, pp. 232-244, 2002. https://dl.acm.org/doi/10.5555/936348
[20] R. Trivedi, H. J. Dai, Y. C. Wang, and L. Song, “Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs” Proc. of the 34th Int. Conf. on Machine Learning, pp. 3462-3471, 2017. https://doi.org/10.48550/arXiv.1705.05742
[21] Y. K. Lin, Z. Y. Liu, M. S. Sun, Y. Liu, and X. Zhu, “Learning Entity and Relation Embeddings for Knowledge Graph Completion,” Proc. of the Twenty-Ninth AAAI Conf. on Artificial Intelligence, pp. 2181-2187, 2015.
[22] H. Xiao, M. L. Huang, H. Yu, and X. Y. Zhu, “TransA: An Adaptive Approach for Knowledge Graph Embedding,” arXiv:1509.05490, 2015. https://doi.org/10.48550/arXiv.1509.05490
[23] G. L. Ji, S. Z. He, L. H. Xu, K. Liu, and J. Zhao, “Knowledge Graph Embedding via Dynamic Mapping Matrix,” Meeting of the Association for Computational Linguistics & the Int. Joint Conf. on Natural Language Processing, 2015. https://doi.org/10.3115/v1/P15-1067
[24] H. Xiao, M. L. Huang, and X. Y. Zhu, “TransG: A Generative Model for Knowledge Graph Embedding,” Proc. of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 2316-2325, 2016. https://doi.org/10.18653/v1/P16-1219

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Y. P. Sheng, “Research and Application of Learning Methods for Knowledge Graphs,” University of Electronic Science and Technology of China, Ph.D. Dissertation, 2020 (in Chinese). https://www.doi.org/10.27005/d.cnki.gdzku.2020.004504.

[2] [2] S. Sen, “Research on Temporal Knowledge Graph Completion Algorithm Based on Knowledge Representation Learning,” University of Electronic Science and Technology of China, M.A. Thesis, 2022 (in Chinese). https://www.doi.org/10.27005/d.cnki.gdzku.2022.001827

[3] [3] L. K. Zhou, Y. Yang, X. Ren, F. Wu, and Y. T. Zhang, “Dynamic Network Embedding by Modeling Triadic Closure Process,” Thirty-Second AAAI Conf. on Artificial Intelligence, Vol.32, No.1, 2018. https://doi.org/10.1609/aaai.v32i1.11257

[4] [4] S. D. Winter, T. Decuypere, S. Mitrović, B. Baesens, and J. D. Weerdt, “Combining Temporal Aspects of Dynamic Networks with Node2Vec for a More Efficient Dynamic Link Prediction,” 2018 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining, pp. 1234-1241, 2018. https://doi.org/10.1109/ASONAM.2018.8508272

[5] [5] Z. Y. Qu and P. F. Sun, “A Method of Knowledge Representation in Power Grid Domain Based on OWL,” J. of Northeast Dianli University, Vol.32, No.4, pp. 30-34, 2012 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2012.04.008

[6] [6] P. Zhou, “Embedding model-based Research on Knowledge Graph Completion Method,” Xidian University, M.A. Thesis, 2020 (in Chinese). https://doi.org/10.27389/d.cnki.gxadu.2020.001326

[7] [7] D. Y. Li, “Research on Knowledge Graph Completion Technology Based on Representation Learning,” Zhengzhou University of Light Industry, M.A. Thesis, 2022 (in Chinese). https://doi.org/10.27469/d.cnki.gzzqc.2022.000028

[8] [8] C. Y. Sun, “The Research and Application of Entity and Relation Completion Based on Knowledge Graph,” University of Electronic Science and Technology of China, M.A. Thesis, 2022 (in Chinese). https://doi.org/10.27005/d.cnki.gdzku.2022.002303

[9] [9] Z. W. Li, ”Knowledge Graph Completion Model Based On Importance Priority,” Harbin Institute of Technology, M.A. Thesis, 2020 (in Chinese). https://doi.org/0.27061/d.cnki.ghgdu.2021.000635

[10] [10] M. Nayyeri, S. Vahdati, J. Lehmann, and H. S. Yazdi, “Soft Marginal TransE for Scholarly Knowledge Graph Completion,” arXiv:1904.12211, 2019. https://doi.org/10.48550/arXiv.1904.12211

[11] [11] Z. Y. Qu, X. D. Fan, H. T. Yu, and N. Qu, “Smart Grid Text Knowledge Acquisition Model Based on Ontology,” J. Of Northeast Dianli University, Vol.34, No.5, pp, 60-68, 2014 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2014.05.012

[12] [12] X. L. Guo and X. Han, “Grid Knowledge Collaborative Discovery Strategy Research,” J. of Northeast Dianli University, Vol.34, No.1, pp. 94-98, 2014 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2014.01.019

[13] [13] Y. H. Wang, F. Yang, and S. Chen, “Power Virtual Communities-oriented Collection Methods of Tacit Knowledge,” J. of Northeast Dianli University, Vol.34, No.4, pp. 69-74, 2014 (in Chinese). https://doi.org/10.19718/j.issn.1005-2992.2014.04.014

[14] [14] A. Bordes, N. Usunier, A. G. Durán, J. Weston, and O. Yakhnenko, “Translating Embeddings for Modeling Multi-relational Data,” Neural Information Processing Systems, pp. 2787-2795, 2013. https://api.semanticscholar.org/CorpusID:14941970

[15] [15] Z. Wang, J. W. Zhang, J. L. Feng, and Z. Chen, “Knowledge Graph Embedding by Translating on Hyperplanes,” AAAI Conf. on Artificial Intelligence, pp. 1112-1119, 2014. https://doi.org/10.1609/aaai.v28i1.8870

[16] [16] T. S. Jiang, T. Y. Liu, T. Ge, L. Sha, B. B. Chang, S. J. Li, and Z. F. Sui, “Towards Time-Aware Knowledge Graph Completion,” Proc. of COLING 2016, the 26th Int. Conf. on Computational Linguistics: Technical Papers, pp. 1715-1724. https://aclanthology.org/C16-1161/

[17] [17] J. Leblay and M. W. Chekol, “Deriving Validity Time in Knowledge Graph,” Companion of the Web Conf., pp. 1771-1776, 2018. https://dl.acm.org/doi/pdf/10.1145/3184558.3191639

[18] [18] S. S. Dasgupta, S. N. Ray, and P. Talukdar, “HyTE: Hyperplane-based Temporally Aware Knowledge Graph Embedding,” Proc. of the 2018 Conf. on Empirical Methods in Natural Language Processing, pp. 2001-2011,2018. https://doi.org/10.18653/v1/D18-1225

[19] [19] A. Paccanaro and G. Hinton, “Learning Distributed Representations of Concepts using Linear Relational Embedding,” IEEE Trans. on Knowledge and Data Engineering, Vol.13, No.2, pp. 232-244, 2002. https://dl.acm.org/doi/10.5555/936348

[20] [20] R. Trivedi, H. J. Dai, Y. C. Wang, and L. Song, “Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs” Proc. of the 34th Int. Conf. on Machine Learning, pp. 3462-3471, 2017. https://doi.org/10.48550/arXiv.1705.05742

[21] [21] Y. K. Lin, Z. Y. Liu, M. S. Sun, Y. Liu, and X. Zhu, “Learning Entity and Relation Embeddings for Knowledge Graph Completion,” Proc. of the Twenty-Ninth AAAI Conf. on Artificial Intelligence, pp. 2181-2187, 2015.

[22] [22] H. Xiao, M. L. Huang, H. Yu, and X. Y. Zhu, “TransA: An Adaptive Approach for Knowledge Graph Embedding,” arXiv:1509.05490, 2015. https://doi.org/10.48550/arXiv.1509.05490

[23] [23] G. L. Ji, S. Z. He, L. H. Xu, K. Liu, and J. Zhao, “Knowledge Graph Embedding via Dynamic Mapping Matrix,” Meeting of the Association for Computational Linguistics & the Int. Joint Conf. on Natural Language Processing, 2015. https://doi.org/10.3115/v1/P15-1067

[24] [24] H. Xiao, M. L. Huang, and X. Y. Zhu, “TransG: A Generative Model for Knowledge Graph Embedding,” Proc. of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 2316-2325, 2016. https://doi.org/10.18653/v1/P16-1219

Research on the Knowledge Representation Method of Power News Text Based on Time Hyperplane

Yun Wu*, Ziyi Wang*, Yan Du**, Jieming Yang*, Xinru Liu***, Zhenhong Liu*,†, and Kai Yang*

Yun Wu^, Ziyi Wang^, Yan Du^**, Jieming Yang^*, Xinru Liu^***, Zhenhong Liu^,†, and Kai Yang^