Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge Graph
In the field of military research, manufacturing and management of weapons and equipment are very important. Due to the continuous advancement of science and technology, many military equipment databases have a loose structure, which makes them difficult to be utilized efficiently, resulting in low...
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doaj-d4e75649deec406692f24e6c937b536e2021-03-30T03:39:33ZengIEEEIEEE Access2169-35362020-01-01820058120058810.1109/ACCESS.2020.30348949245495Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge GraphChenguang Liu0https://orcid.org/0000-0002-0058-8073Yongli Yu1Xingxin Li2Peng Wang3https://orcid.org/0000-0002-5931-8852Army Engineering University of the People’s Liberation Army, Shijiazhuang, ChinaArmy Engineering University of the People’s Liberation Army, Shijiazhuang, ChinaArmy Engineering University of the People’s Liberation Army, Shijiazhuang, ChinaArmy Engineering University of the People’s Liberation Army, Shijiazhuang, ChinaIn the field of military research, manufacturing and management of weapons and equipment are very important. Due to the continuous advancement of science and technology, many military equipment databases have a loose structure, which makes them difficult to be utilized efficiently, resulting in low efficiency, chaotic management, and other issues. In order to solve these problems, an entity-relation extraction method based on CRF and syntactic analysis tree is proposed according to the latest text extraction algorithm. Finally, a military knowledge graph construction method is optimized via massive data training, model comparison and improvement. The ternary data extraction method is significantly better than the single algorithm extraction method, and the accuracy of the extracted training model can reach 72%. Compared with the traditional entity-relation extraction method, the accuracy of the entity-relation extraction method based on the fusion of CRF and syntax analysis tree is improved by 12.6% when the confidence model is added, and the comprehensive evaluation accuracy can reach 78.11%. This result has significant practical value for the construction of knowledge graphs in the field of military equipment.https://ieeexplore.ieee.org/document/9245495/CRFmilitary equipmentknowledge graphentity relation extractionconfidence model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chenguang Liu Yongli Yu Xingxin Li Peng Wang |
spellingShingle |
Chenguang Liu Yongli Yu Xingxin Li Peng Wang Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge Graph IEEE Access CRF military equipment knowledge graph entity relation extraction confidence model |
author_facet |
Chenguang Liu Yongli Yu Xingxin Li Peng Wang |
author_sort |
Chenguang Liu |
title |
Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge Graph |
title_short |
Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge Graph |
title_full |
Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge Graph |
title_fullStr |
Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge Graph |
title_full_unstemmed |
Application of Entity Relation Extraction Method Under CRF and Syntax Analysis Tree in the Construction of Military Equipment Knowledge Graph |
title_sort |
application of entity relation extraction method under crf and syntax analysis tree in the construction of military equipment knowledge graph |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
In the field of military research, manufacturing and management of weapons and equipment are very important. Due to the continuous advancement of science and technology, many military equipment databases have a loose structure, which makes them difficult to be utilized efficiently, resulting in low efficiency, chaotic management, and other issues. In order to solve these problems, an entity-relation extraction method based on CRF and syntactic analysis tree is proposed according to the latest text extraction algorithm. Finally, a military knowledge graph construction method is optimized via massive data training, model comparison and improvement. The ternary data extraction method is significantly better than the single algorithm extraction method, and the accuracy of the extracted training model can reach 72%. Compared with the traditional entity-relation extraction method, the accuracy of the entity-relation extraction method based on the fusion of CRF and syntax analysis tree is improved by 12.6% when the confidence model is added, and the comprehensive evaluation accuracy can reach 78.11%. This result has significant practical value for the construction of knowledge graphs in the field of military equipment. |
topic |
CRF military equipment knowledge graph entity relation extraction confidence model |
url |
https://ieeexplore.ieee.org/document/9245495/ |
work_keys_str_mv |
AT chenguangliu applicationofentityrelationextractionmethodundercrfandsyntaxanalysistreeintheconstructionofmilitaryequipmentknowledgegraph AT yongliyu applicationofentityrelationextractionmethodundercrfandsyntaxanalysistreeintheconstructionofmilitaryequipmentknowledgegraph AT xingxinli applicationofentityrelationextractionmethodundercrfandsyntaxanalysistreeintheconstructionofmilitaryequipmentknowledgegraph AT pengwang applicationofentityrelationextractionmethodundercrfandsyntaxanalysistreeintheconstructionofmilitaryequipmentknowledgegraph |
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1724183121002233856 |