Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain
Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relie...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/7/1097 |
| _version_ | 1797589358236139520 |
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| author | Siqing Wei Yanchun Liang Xiaoran Li Xiaohui Weng Jiasheng Fu Xiaosong Han |
| author_facet | Siqing Wei Yanchun Liang Xiaoran Li Xiaohui Weng Jiasheng Fu Xiaosong Han |
| author_sort | Siqing Wei |
| collection | DOAJ |
| description | Managed pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference. |
| first_indexed | 2024-03-11T01:05:30Z |
| format | Article |
| id | doaj.art-348d78775e2c490b81845d16c35b9ccc |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T01:05:30Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-348d78775e2c490b81845d16c35b9ccc2023-11-18T19:14:46ZengMDPI AGEntropy1099-43002023-07-01257109710.3390/e25071097Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling DomainSiqing Wei0Yanchun Liang1Xiaoran Li2Xiaohui Weng3Jiasheng Fu4Xiaosong Han5Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaKey Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaKey Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaSchool of Mechanical and Aerospace Engineering, Jilin University, Changchun 130012, ChinaCNPC Engineering Technology R&D Company Limited, National Engineering Research Center of Oil & Gas Drilling and Completion Technology, Beijing 102206, ChinaKey Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaManaged pressure drilling (MPD) is the most effective means to ensure drilling safety, and MPD is able to avoid further deterioration of complex working conditions through precise control of the wellhead back pressure. The key to the success of MPD is the well control strategy, which currently relies heavily on manual experience, hindering the automation and intelligence process of well control. In response to this issue, an MPD knowledge graph is constructed in this paper that extracts knowledge from published papers and drilling reports to guide well control. In order to improve the performance of entity extraction in the knowledge graph, a few-shot Chinese entity recognition model CEntLM-KL is extended from the EntLM model, in which the KL entropy is built to improve the accuracy of entity recognition. Through experiments on benchmark datasets, it has been shown that the proposed model has a significant improvement compared to the state-of-the-art methods. On the few-shot drilling datasets, the F-1 score of entity recognition reaches 33%. Finally, the knowledge graph is stored in Neo4J and applied for knowledge inference.https://www.mdpi.com/1099-4300/25/7/1097MPDknowledge graphsentity extractionrelation extractionfew shot |
| spellingShingle | Siqing Wei Yanchun Liang Xiaoran Li Xiaohui Weng Jiasheng Fu Xiaosong Han Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain Entropy MPD knowledge graphs entity extraction relation extraction few shot |
| title | Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain |
| title_full | Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain |
| title_fullStr | Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain |
| title_full_unstemmed | Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain |
| title_short | Chinese Few-Shot Named Entity Recognition and Knowledge Graph Construction in Managed Pressure Drilling Domain |
| title_sort | chinese few shot named entity recognition and knowledge graph construction in managed pressure drilling domain |
| topic | MPD knowledge graphs entity extraction relation extraction few shot |
| url | https://www.mdpi.com/1099-4300/25/7/1097 |
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