Power entity recognition based on bidirectional long short-term memory and conditional random fields
With the application of artificial intelligence technology in the power industry, the knowledge graph is expected to play a key role in power grid dispatch processes, intelligent maintenance, and customer service response provision. Knowledge graphs are usually constructed based on entity recognitio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2020-04-01
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| Series: | Global Energy Interconnection |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096511720300529 |
| _version_ | 1818350483908067328 |
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| author | Zhixiang Ji Xiaohui Wang Changyu Cai Hongjian Sun |
| author_facet | Zhixiang Ji Xiaohui Wang Changyu Cai Hongjian Sun |
| author_sort | Zhixiang Ji |
| collection | DOAJ |
| description | With the application of artificial intelligence technology in the power industry, the knowledge graph is expected to play a key role in power grid dispatch processes, intelligent maintenance, and customer service response provision. Knowledge graphs are usually constructed based on entity recognition. Specifically, based on the mining of entity attributes and relationships, domain knowledge graphs can be constructed through knowledge fusion. In this work, the entities and characteristics of power entity recognition are analyzed, the mechanism of entity recognition is clarified, and entity recognition techniques are analyzed in the context of the power domain. Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated, and the two methods are comparatively analyzed. The results indicated that the CRF model, with an accuracy of 83%, can better identify the power entities compared to the BLSTM. The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field. |
| first_indexed | 2024-12-13T18:22:34Z |
| format | Article |
| id | doaj.art-2f36e0bc1ca447b29377fdc746d43840 |
| institution | Directory Open Access Journal |
| issn | 2096-5117 |
| language | English |
| last_indexed | 2024-12-13T18:22:34Z |
| publishDate | 2020-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Global Energy Interconnection |
| spelling | doaj.art-2f36e0bc1ca447b29377fdc746d438402022-12-21T23:35:41ZengKeAi Communications Co., Ltd.Global Energy Interconnection2096-51172020-04-0132186192Power entity recognition based on bidirectional long short-term memory and conditional random fieldsZhixiang Ji0Xiaohui Wang1Changyu Cai2Hongjian Sun3China Electric Power Research Institute Co. Ltd., Beijing, 100192, P.R. China; Corresponding author.China Electric Power Research Institute Co. Ltd., Beijing, 100192, P.R. ChinaChina Electric Power Research Institute Co. Ltd., Beijing, 100192, P.R. ChinaUniversity of Durham, The Palatine Centre, Stockton Road, Durham, DH1 3LE, UKWith the application of artificial intelligence technology in the power industry, the knowledge graph is expected to play a key role in power grid dispatch processes, intelligent maintenance, and customer service response provision. Knowledge graphs are usually constructed based on entity recognition. Specifically, based on the mining of entity attributes and relationships, domain knowledge graphs can be constructed through knowledge fusion. In this work, the entities and characteristics of power entity recognition are analyzed, the mechanism of entity recognition is clarified, and entity recognition techniques are analyzed in the context of the power domain. Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated, and the two methods are comparatively analyzed. The results indicated that the CRF model, with an accuracy of 83%, can better identify the power entities compared to the BLSTM. The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.http://www.sciencedirect.com/science/article/pii/S2096511720300529Knowledge graphEntity recognitionConditional Random Fields (CRF)Bidirectional Long Short-Term Memory (BLSTM) |
| spellingShingle | Zhixiang Ji Xiaohui Wang Changyu Cai Hongjian Sun Power entity recognition based on bidirectional long short-term memory and conditional random fields Global Energy Interconnection Knowledge graph Entity recognition Conditional Random Fields (CRF) Bidirectional Long Short-Term Memory (BLSTM) |
| title | Power entity recognition based on bidirectional long short-term memory and conditional random fields |
| title_full | Power entity recognition based on bidirectional long short-term memory and conditional random fields |
| title_fullStr | Power entity recognition based on bidirectional long short-term memory and conditional random fields |
| title_full_unstemmed | Power entity recognition based on bidirectional long short-term memory and conditional random fields |
| title_short | Power entity recognition based on bidirectional long short-term memory and conditional random fields |
| title_sort | power entity recognition based on bidirectional long short term memory and conditional random fields |
| topic | Knowledge graph Entity recognition Conditional Random Fields (CRF) Bidirectional Long Short-Term Memory (BLSTM) |
| url | http://www.sciencedirect.com/science/article/pii/S2096511720300529 |
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