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PetroKG: Construction and Application of Knowledge Graph in Upstream Area of PetroChina

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Abstract

There is a large amount of heterogeneous data distributed in various sources in the upstream of PetroChina. These data can be valuable assets if we can fully use them. Meanwhile, the knowledge graph, as a new emerging technique, provides a way to integrate multi-source heterogeneous data. In this paper, we present one application of the knowledge graph in the upstream of PetroChina. Specifically, we first construct a knowledge graph from both structured and unstructured data with multiple NLP (natural language progressing) methods. Then, we introduce two typical knowledge graph powered applications and show the benefit that the knowledge graph brings to these applications: compared with the traditional machine learning approach, the well log interpretation method powered by knowledge graph shows more than 7.69% improvement of accuracy.

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References

  1. Kazama J, Torisawa K. Exploiting Wikipedia as external knowledge for named entity recognition. In Proc. the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 2007, pp.698-003707.

  2. Ratinov L, Roth D. Design challenges and misconceptions in named entity recognition. In Proc. the 13th Conference on Computational Natural Language Learning, Association for Computational Linguistics, June 2009, pp.147-155.

  3. Luo G, Huang X, Lin C Y, Nie Z. Joint entity recognition and disambiguation. In Proc. the 2015 Conference on Empirical Methods in Natural Language Processing, September 2015, pp.879-888.

  4. Limsopatham N, Collier N. Bidirectional LSTM for named entity recognition in Twitter messages. In Proc. the 2nd Workshop on Noisy User-Generated Text, December 2016, pp.145-152.

  5. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C. Neural architectures for named entity recognition. In Proc. the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2016, pp.260-270.

  6. Ma X, Hovy E. End-to-end sequence labeling via bidirectional LSTM-CNNs-CRF. In Proc. the 54th Annual Meeting of the Association for Computational Linguistics, August 2016, pp.1064-1074.

  7. Devlin J, Chang M W, Lee K, Toutanova K. BERT: Pretraining of deep bidirectional transformers for language understanding. In Proc. the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2019, pp.4171-4186.

  8. Angeli G, Premkumar M J J, Manning C D. Leveraging linguistic structure for open domain information extraction. In Proc. the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, July 2015, pp.344-354.

  9. Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka E R, Mitchell T M. Toward an architecture for never-ending language learning. In Proc. the 24th AAAI Conference on Artificial Intelligence, July 2010, pp.1306-1313.

  10. Mitchell T, Fredkin E. Never-ending language learning. In Proc. the 2014 IEEE International Conference on Big Data, October 2014.

  11. Christensen J, Soderland S, Etzioni O. Semantic role labeling for open information extraction. In Proc. the 1st NAACL HLT International Workshop on Formalisms and Methodology for Learning by Reading, June 2010, pp.52-60.

  12. Christensen J, Mausam, Soderland S, Etzioni O. An analysis of open information extraction based on semantic role labeling. In Proc. the 6th International Conference on Knowledge Capture, June 2011, pp.113-120.

  13. Santos C N D, Xiang B, Zhou B. Classifying relations by ranking with convolutional neural networks. arXiv:1504.06580, 2015. https://arxiv.org/pdf/1504.065-80.pdf, Nov. 2019.

  14. Wang L, Cao Z, de Melo G, Liu Z. Relation classification via multi-level attention CNNs. In Proc. the 54th Annual Meeting of the Association for Computational Linguistics, August 2016, pp.1298-1307.

  15. Zeng D, Liu K, Chen Y, Zhao J. Distant supervision for relation extraction via piecewise convolutional neural networks. In Proc. the 2015 Conference on Empirical Methods in Natural Language Processing, September 2015, pp.1753-1762.

  16. Miwa M, Bansal M. End-to-end relation extraction using LSTMs on sequences and tree structures. arXiv:1601.00770, 2016. https://arxiv.org/abs/1601.00770, Nov. 2019.

  17. Zheng S, Hao Y, Lu D, Bao H, Xu J, Hao H, Xu B. Joint entity and relation extraction based on a hybrid neural network. Neurocomputing, 2017, 257: 59-66.

    Article  Google Scholar 

  18. Zheng S, Wang F, Bao H, Hao Y, Zhou P, Xu B. Joint extraction of entities and relations based on a novel tagging scheme. arXiv:1706.05075 June 2017. https://arxiv.org/abs/1706.05075, Nov. 2019.

  19. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In Proc. the 27th Annual Conference on Neural Information Processing Systems, December 2013, pp.2787-2795.

  20. Wang Z, Zhang J, Feng J, Chen Z. Knowledge graph embedding by translating on hyperplanes. In Proc. the 28th AAAI Conference on Artificial Intelligence, June 2014, pp.1112-1119.

  21. Lin Y, Liu Z, Sun M, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In Proc. the 29th AAAI Conference on Artificial Intelligence, February 2015, pp.2181-2187.

  22. Ji G, He S, Xu L, Liu K, Zhao J. Knowledge graph embedding via dynamic mapping matrix. In Proc. the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, July 2015, pp.687-696.

  23. Liu Q, Huang Z, Yin Y, Chen E, Xiong H, Su Y, Hu G. EKT: Exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2019.2924374.

  24. Huang Z, Liu Q, Chen E, Zhao H, Gao M, Wei S, Su Y, Hu G. Question difficulty prediction for READING problems in standard tests. In Proc. the 31st AAAI Conference on Artificial Intelligence, February 2017, pp.1352-1359.

  25. Yuan J, Zheng Y, Xie X. Discovering regions of different functions in a city using human mobility and POIs. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2012, pp.186-194.

  26. Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y. T-drive: Driving directions based on taxi trajectories. In Proc. the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2010, pp.99-108.

  27. He Y, Mendis G J, Wei J. Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism. IEEE Transactions on Smart Grid, 2017, 8(5): 2505-2516.

    Article  Google Scholar 

  28. Layouni M, Tahar S, Hamdi M S. A survey on the application of neural networks in the safety assessment of oil and gas pipelines. In Proc. the 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, December 2014, pp.95-102.

  29. Mohamed A, Hamdi M S, Tahar S. A machine learning approach for big data in oil and gas pipelines. In Proc. the 3rd International Conference on Future Internet of Things and Cloud, August 2015, pp.585-590.

  30. Priyadarshy S, Taylor A, Dev A, Venugopal S, Nair G G. Framework for prediction of NPT causes using unstructured reports. In Proc. the 2017 Offshore Technology Conference, May 2017.

  31. Hoffimann J, Mao Y, Wesley A, Taylor A. Sequence mining and pattern analysis in drilling reports with deep natural language processing. arXiv:1712.01476, 2017. https://arxiv.org/pdf/1712.01476.pdf, Nov. 2019.

  32. Ma Z, Vajargah A K, Lee H, Darabi H, Castineira D. Applications of machine learning and data mining in SpeedWise® drilling analytics: A case study. In Proc. the 2018 Abu Dhabi International Petroleum Exhibition & Conference, November 2018.

  33. Ge J, Li Z, Li T, Qiang B. Petroleum exploration domain ontology-based knowledge integration and sharing system construction. In Proc. the 2011 International Conference on Network Computing and Information Security, May 2011, pp.84-88.

  34. Che W, Li Z, Liu T. LTP: A Chinese language technology platform. In Proc. the 23rd International Conference on Computational Linguistics: Demonstrations, August 2010, pp.13-16.

  35. Bing Q, Anan L, Ting L. Unsupervised Chinese open entity relation extraction. Jurnal of Computer Research and Development, 2015, 52(5): 1029-1035. (in Chinese)

  36. Keller J M, Gray M R, Givens J A. A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 1985, 15(4): 580-585.

    Article  Google Scholar 

  37. Pal M. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 2005, 26 (1): 217-222.

    Article  Google Scholar 

  38. Pal S K, Mitra S. Multilayer perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks, 1992, 3(5): 683-697.

    Article  Google Scholar 

  39. Friedman J H. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 2001, 29(5): 1189-1232.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. PetroChina Research Institute of Petroleum Exploration and Development, Beijing, 100083, China

    Xiang-Guang Zhou, Ren-Bin Gong & Fu-Geng Shi

  2. Huawei Technologies, Hangzhou, 310007, China

    Zhe-Feng Wang

Authors
  1. Xiang-Guang Zhou
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  2. Ren-Bin Gong
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  3. Fu-Geng Shi
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  4. Zhe-Feng Wang
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Correspondence to Xiang-Guang Zhou.

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Zhou, XG., Gong, RB., Shi, FG. et al. PetroKG: Construction and Application of Knowledge Graph in Upstream Area of PetroChina. J. Comput. Sci. Technol. 35, 368–378 (2020). https://doi.org/10.1007/s11390-020-9966-7

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  • DOI: https://doi.org/10.1007/s11390-020-9966-7

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