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A lattice LSTM-based framework for knowledge graph construction from power plants maintenance reports

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Abstract

Historical experience plays a significant role in the intelligent maintenance of power plants. While maintaining power equipment, engineers would record the experience in maintenance documents called status reports. Through decades of maintenance, massive status reports have been accumulated. These text data contains rich knowledge about power equipment, and they can be a strong support for intelligent maintenance. However, to fully utilize the knowledge from these reports is not easy because of two main reasons. First, there are a huge amount of data, making it difficult to find the specific knowledge we want. Second, the knowledge contained in reports is unorganized, and few previous works have been attempted to automatically mine the knowledge from these text data. To address this problem, we propose an innovative framework for automatic construction and reasoning of Chinese knowledge graph toward intelligent maintenance of power plants. In this framework, the lattice LSTM and multi-grained lattice framework (MG lattice) are adopted to extract entities and relations respectively from text data. What’s more, we present a dataset for Chinese Named Entity Recognition, which contains four categories of entities and consists of 864 sentences from status reports. Comprehensive experiments are carried out on this dataset. The experimental results show that the lattice LSTM method is significantly superior to classic LSTM-CRF model on power plant maintenance data, implying the effectiveness and potential of our proposed framework.

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References

  1. Chong Z (2016) Text classification based on attention based lstm model. Doctoral dissertation

  2. Curran JR, Clark S (2003) Language independent ner using a maximum entropy tagger. In: Proceedings of the seventh conference on natural language learning at HLT-NAACL 2003:164–167

  3. Ding X, Zhang Y, Liu T, et al (2016) Knowledge-driven event embedding for stock prediction. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers. The COLING 2016 Organizing Committee, Osaka, Japan, pp 2133–2142, https://aclanthology.org/C16-1201

  4. Dong Z, Dong Q (2003) Hownet-a hybrid language and knowledge resource. In: International conference on natural language processing and knowledge engineering, 2003. Proceedings. 2003, IEEE, pp 820–824

  5. Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw 18(5–6):602–610

    Article  Google Scholar 

  6. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  7. Hogan A, Blomqvist E, Cochez M et al (2021) Knowledge graphs. ACM Comput Surv. https://doi.org/10.1145/3447772

    Article  Google Scholar 

  8. Huang S, Sun X, Wang H (2017) Addressing domain adaptation for Chinese word segmentation with global recurrent structure. In: Proceedings of the eighth international joint conference on natural language processing (Volume 1: Long Papers). Asian Federation of Natural Language Processing, Taipei, Taiwan, pp 184–193, https://aclanthology.org/I17-1019

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

    Article  Google Scholar 

  10. Lample G, Ballesteros M, Subramanian S, et al (2016) Neural architectures for named entity recognition. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, California, pp 260–270, https://doi.org/10.18653/v1/N16-1030, https://aclanthology.org/N16-1030

  11. Levow GA (2006) The third international chinese language processing bakeoff: word segmentation and named entity recognition. In: Proceedings of the Fifth SIGHAN workshop on Chinese language processing, pp 108–117

  12. Li H, Hagiwara M, Li Q, et al (2014) Comparison of the impact of word segmentation on name tagging for Chinese and Japanese. In: Proceedings of the ninth international conference on language resources and evaluation (LREC’14), pp 2532–2536

  13. Li X, Meng Y, Sun X, et al (2019a) Is word segmentation necessary for deep learning of Chinese representations? In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, Italy, pp 3242–3252, https://doi.org/10.18653/v1/P19-1314

  14. Li Z, Ding N, Liu Z, et al (2019b) Chinese relation extraction with multi-grained information and external linguistic knowledge. In: Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, Italy, pp 4377–4386, https://doi.org/10.18653/v1/P19-1430, https://aclanthology.org/P19-1430

  15. Liu C, Sun W, Chao W, et al (2013) Convolution neural network for relation extraction. In: International conference on advanced data mining and applications, Springer, pp 231–242

  16. Luo R, Xu J, Zhang Y, et al (2019) Pkuseg: A toolkit for multi-domain Chinese word segmentation. arXiv preprint arXiv:1906.11455

  17. Mikolov T, Sutskever I, Chen K et al (2013) Distributed representations of words and phrases and their compositionality. In: Burges CJC, Bottou L, Welling M et al (eds) Advances in neural information processing systems. Curran Associates Inc., Red Hook

    Google Scholar 

  18. Miwa M, Bansal M (2016) End-to-end relation extraction using LSTMs on sequences and tree structures. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, pp 1105–1116. https://doi.org/10.18653/v1/P16-1105https://aclanthology.org/P16-1105

  19. Niu Y, Xie R, Liu Z, et al (2017) Improved word representation learning with sememes. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, pp 2049–2058. https://doi.org/10.18653/v1/P17-1187, https://aclanthology.org/P17-1187

  20. Rink B, Harabagiu S (2010) Utd: Classifying semantic relations by combining lexical and semantic resources. In: Proceedings of the 5th international workshop on semantic evaluation, pp 256–259

  21. Rotmensch M, Halpern Y, Tlimat A et al (2017) Learning a health knowledge graph from electronic medical records. Sci Rep 7(1):1–11

    Article  Google Scholar 

  22. Wu H, Yan Y, Ye Y et al (2020) Geometric knowledge embedding for unsupervised domain adaptation. Knowledge-Based Syst 191(105):155. https://doi.org/10.1016/j.knosys.2019.105155

    Article  Google Scholar 

  23. Zeng D, Liu K, Lai S, et al (2014) Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers. Dublin City University and Association for Computational Linguistics, Dublin, Ireland, pp 2335–2344, https://aclanthology.org/C14-1220

  24. Zeng D, Liu K, Chen Y, et al (2015) Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 conference on empirical methods in natural language processing. Association for Computational Linguistics, Lisbon, Portugal, pp 1753–1762, https://doi.org/10.18653/v1/D15-1203, https://aclanthology.org/D15-1203

  25. Zhang Y, Yang J (2018) Chinese NER using lattice LSTM. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, pp 1554–1564, https://doi.org/10.18653/v1/P18-1144, https://aclanthology.org/P18-1144

  26. Zhou G, Su J, Zhang J, et al (2005) Exploring various knowledge in relation extraction. In: Proceedings of the 43rd annual meeting of the association for computational linguistics (ACL’05). Association for Computational Linguistics, Ann Arbor, Michigan, pp 427–434, https://doi.org/10.3115/1219840.1219893, https://aclanthology.org/P05-1053

  27. Zhou P, Shi W, Tian J, et al (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Berlin, Germany, pp 207–212, https://doi.org/10.18653/v1/P16-2034, https://aclanthology.org/P16-2034

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Acknowledgements

This work was supported by the National Key RD Program of China Under Grant No. 2020YFB1707803. This work was also supported in part by the Zhejiang University/University of Illinois at Urbana-Champaign Institute, and was led by Principal Supervisor Prof. Hongwei Wang. In addition, part of this paper is extended from a conference paper originally presented in the IEEE ICEBE 2021 conference. The authors also would like to thank the conference organizers for their invitation to extend the paper.

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  1. ZJU-UIUC Institute, Zhejiang University, Hangzhou, Zhejiang, China

    Tingyu Xie, Shuting Tao, Qi Li, Hongwei Wang & Yihong Jin

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  1. Tingyu Xie
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  2. Shuting Tao
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  3. Qi Li
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Correspondence to Hongwei Wang.

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Xie, T., Tao, S., Li, Q. et al. A lattice LSTM-based framework for knowledge graph construction from power plants maintenance reports. SOCA 16, 167–177 (2022). https://doi.org/10.1007/s11761-022-00338-4

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