In order to build an aviation assembly knowledge graph composed of assembly process information, assembly technology knowledge, related industry standards and internal connections of the three, a named entity recognition technology framework based on continual learning was proposed. The characteristic of the proposed framework was that it maintained high recognition performance throughout the progressive learning process from zero corpus to large-scale corpus, without relying on manual feature setting. A comparative performance experiment of the proposed framework was carried out in practical industrial scenarios, the experiment proceeded from general assembly and component assembly, and the manipulations of the pull rod and cable installation were regard as a specific experimental case. Experimental results show that the proposed framework is significantly better in accuracy, recall, and F1 value than previous algorithms, while handling different-scale corpus environments. And the credible results for named entity recognition tasks can be provided consistently by the proposed framework in the aviation assembly domain.
Pei-feng LIU,Lu QIAN,Xing-wei ZHAO,Bo TAO. Continual learning framework of named entity recognition in aviation assembly domain. Journal of ZheJiang University (Engineering Science), 2023, 57(6): 1186-1194.
Tab.1Experimental results of named entity recognition models on MSRA corpus
航空装配本体
现有知识库本体
组件实体（component）
产品实体（product）
固定设施实体（facility）
固定平台实体（plant）
操作项目实体（operation）
工序实体（process）
工序步骤实体（step）
工序计划实体（process plan）
工具实体（tool）
工具实体（tool）
Tab.2Comparison table of aviation assembly ontology and existing knowledge ontology
Fig.1Example of component entities in aviation assembly domain
Fig.2Human-computer interaction tool interface
实体类别
nce
AA-1
AA-2
总计
组件实体（component）
336
213
549
固定设施实体（facility）
92
3
95
操作项目实体（operation）
263
91
354
工序步骤实体（step）
122
6
128
工具实体（tool）
139
18
157
Tab.3Statistics of corpus entity count
Fig.3Continual learning framework for named entity recognition
Fig.4Basic structure of long-short term memory unit
Fig.5Basic structure of character-based bidirectional long-short term memory with conditional random field model
语料库
n
标注方式
训练集
验证集
测试集
MSRA
1 921 489
246 370
229 910
BIO
AA-1
9 364
621
665
BMEO
AA-2
13 068
709
653
BMEO
Tab.4Corpus information statistics
n
F1/%
HMM
CRF
BiLSTM
BiLSTM+CRF
本研究
5 468
89.20
87.71
87.25
87.24
89.20
10 437
89.49
88.20
87.33
87.31
89.49
20 201
90.44
89.05
87.49
88.80
90.44
40 061
91.44
90.63
90.66
90.59
91.66
80 095
92.74
92.79
91.28
92.50
93.05
1 921 489
94.82
98.05
97.51
98.02
98.21
Tab.5F1-score of different models on MSRA corpus
Fig.6Variation curves of F1 values and training word counts on MSRA corpus
模型
P/%
R/%
F1/%
HMM
94.76
94.90
94.82
CRF
98.03
98.07
98.05
BiLSTM
97.49
97.54
97.51
BiLSTM+CRF
98.01
98.04
98.02
本研究
98.19
98.23
98.21
Tab.6Experimental results of different models on MSRAcorpus
实体类别
nce,t
AA-1
AA-2
组件实体（component）
27
42
固定设施实体（facility）
6
—
操作项目实体（operation）
19
29
工序步骤实体（step）
10
—
工具实体（tool）
2
4
Tab.7Number of entities in test set for two aviation assembly corpuses
模型
P/%
R/%
F1/%
HMM
89.94
86.62
88.24
CRF
86.33
85.85
86.09
BiLSTM
76.35
78.46
77.39
BiLSTM+CRF
74.44
78.15
76.25
本研究
89.94
86.62
88.24
Tab.8Experimental results of different models on AA-1 corpus
实体类别
P/%
R/%
F1/%
组件实体（component）
75.86
81.48
78.57
固定设施实体（facility）
100
33.33
50.00
操作项目实体（operation）
69.23
94.74
80.00
工序步骤实体（step）
75.00
90.00
81.82
工具实体（tool）
12.50
50.00
20.00
Tab.9Experimental results of continual learning framework on five entities from AA-1 corpus
模型
P/%
R/%
F1/%
HMM
85.12
86.39
85.75
CRF
84.03
85.60
84.81
BiLSTM
79.98
82.75
81.34
BiLSTM+CRF
79.61
81.80
80.69
本研究
85.12
86.39
85.75
Tab.10Experimental results of different models on AA-2 corpus
实体类别
P/%
R/%
F1/%
组件实体（component）
83.33
71.43
76.77
操作项目实体（operation）
79.31
79.31
79.31
工具实体（tool）
66.67
50.00
57.14
Tab.11Experimental results of continual learning framework on three entities from AA-2 corpus
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