Abstract
The safety of electric power grids can be threatened by defects in main electrical equipment, creating significant risks and pressures for dispatching operations. To analyze defects in main electrical equipment, we adopt a knowledge graph link prediction approach. We found that using pre-training models, such as BERT, to extract node features and embed initial embeddings significantly improves the effectiveness of knowledge graph embedding models (KGEMs). However, this approach may not always work and could lead to performance degradation. To address this, we propose a transfer learning method that utilizes a small amount of domain-specific electric power corpus to fine-tune the pre-training model. The PCA algorithm is used to reduce the dimensionality of extracted features, thereby lowering the computational cost of KGEMs. Experimental results show that our model effectively improves link prediction performance in analyzing defects in main electrical equipment.
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References
Fan, S., Liu, X., Chen, Y., et al.: How to construct a power knowledge graph with dispatching data? Sci. Program. 2020, 1–10 (2020)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390(6), 1150–1170 (2011)
Wang, Q., Mao, Z., Wang, B., et al.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)
Devlin, J., Chang, M.W., Lee, K., et al.: Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 (2018)
Liu, Y., Ott, M., Goyal, N., et al.: Roberta: A Robustly Optimized Bert Pretraining Approach. arXiv preprint arXiv:1907.11692 (2019)
Reimers, N., Gurevych, I.: Sentence-bert: Sentence Embeddings Using Siamese Bert-Networks. arXiv preprint arXiv:1908.10084 (2019)
Maćkiewicz, A., Ratajczak, W.: Principal Components Analysis (PCA). Computers & Geosciences 19(3), 303–342 (1993)
Liu, Q., Kusner, M.J., Blunsom, P.: A Survey on Contextual Embeddings. arXiv preprint arXiv:2003.07278 (2020)
Liu, W., Zhou, P., Zhao, Z., et al.: K-bert: Enabling language representation with knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence 34(03), 2901–2908 (2020)
Yao, L., Mao, C., Luo, Y.: KG-BERT: BERT for Knowledge Graph Completion. arXiv preprint arXiv:1909.03193 (2019)
Li, D., Yi, M., He, Y.: Lp-bert: Multi-Task Pre-Training Knowledge Graph Bert for Link Prediction. arXiv preprint arXiv:2201.04843 (2022)
Gururangan, S., Marasović, A., Swayamdipta, S., et al.: Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks. arXiv preprint arXiv:2004.10964 (2020)
Sushil, M., Suster, S., Daelemans, W.: Are we there yet? exploring clinical domain knowledge of BERT models. In: Proceedings of the 20th Workshop on Biomedical Language Processing, pp. 41–53 (2021)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995)
Ali, M., Berrendorf, M., Hoyt, C.T., et al.: PyKEEN 1.0: a python library for training and evaluating knowledge graph embeddings. The Journal of Machine Learning Res. 22(1), 3723–3728 (2021)
Wolf, T., Debut, L., Sanh, V., et al.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45 (2020)
Ji, S., Pan, S., Cambria, E., et al.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE trans. Neural Networks Learning Syst. 33(2), 494–514 (2021)
Wang, Z., Zhang, J., Feng, J., et al.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence 28(1) (2014)
Yang, B., Yih, W., He, X., et al.: Embedding Entities and Relations for Learning and Inference in Knowledge Bases. arXiv preprint arXiv:1412.6575 (2014)
Bordes, A., Usunier, N., Garcia-Duran, A., et al.: Translating embeddings for modeling multi-relational data. Adv. Neural Information Processing Syst. 26 (2013)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a large ontology from wikipedia and wordnet. J. Web Semantics 6(3), 203–217 (2008)
Acknowledgment
This work is supported by Major Program of Xiamen (3502Z20231006); National Natural Science Foundation of China (62176227,U2066213); Fundamental Research Funds for the Central Universities (20720210047).
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Chen, Y. et al. (2023). A Novel Approach to Analyzing Defects: Enhancing Knowledge Graph Embedding Models for Main Electrical Equipment. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_60
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DOI: https://doi.org/10.1007/978-981-99-4761-4_60
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