The Construction of Knowledge Graphs in the Assembly Domain Based on Deep Learning Article Swipe
Jing Qu
,
Yanmei Li
,
Huilong Du
,
Wen Wang
,
Weiping Fu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3576168
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3576168
The operation scenario of human-robot collaboration assembly involves multiple channels to acquire assembly domain knowledge data. In order to equip collaborative robots with certain general knowledge of assembly work and logical reasoning ability, this paper proposes a Chinese-character-based names entity recognition model Bert-BiLSTM-CRF and a relational extraction model Bert-BiGRU-ATT, based on the multiple heterogeneous data in the assembly domain, to construct an assembly domain knowledge graph centered on and for human-robot collaboration. Among them, the Chinese-character-based names entity recognition model avoids the error bias caused by Chinese word segmentation in traditional methods, while the relation extraction model fully extracts the association information between entities and relations. The proposed entity recognition model and relation extraction model are validated in a real scenario of mechanical product assembly. The experimental results show that the proposed models exhibit excellent performance in the human-machine collaborative assembly domain, with overall average F1-score of 84.02% and 94.92% for entities and relations, respectively. The constructed knowledge graph of the assembly domain contains 2724 triples, and the knowledge graph is presented through a visual interactive interface.
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- Type
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- Language
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- https://doi.org/10.1109/access.2025.3576168
- OA Status
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- References
- 28
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- 10
- OpenAlex ID
- https://openalex.org/W4410985953
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410985953Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3576168Digital Object Identifier
- Title
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The Construction of Knowledge Graphs in the Assembly Domain Based on Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Jing Qu, Yanmei Li, Huilong Du, Wen Wang, Weiping FuList of authors in order
- Landing page
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https://doi.org/10.1109/access.2025.3576168Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2025.3576168Direct OA link when available
- Concepts
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Computer science, Deep learning, Knowledge graph, Domain (mathematical analysis), Domain knowledge, Artificial intelligence, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.average | 147 |
| abstract_inverted_index.between | 105 |
| abstract_inverted_index.certain | 23 |
| abstract_inverted_index.domain, | 58, 144 |
| abstract_inverted_index.exhibit | 136 |
| abstract_inverted_index.general | 24 |
| abstract_inverted_index.logical | 30 |
| abstract_inverted_index.overall | 146 |
| abstract_inverted_index.product | 126 |
| abstract_inverted_index.results | 130 |
| abstract_inverted_index.through | 175 |
| abstract_inverted_index.F1-score | 148 |
| abstract_inverted_index.ability, | 32 |
| abstract_inverted_index.assembly | 6, 12, 27, 57, 62, 143, 164 |
| abstract_inverted_index.centered | 66 |
| abstract_inverted_index.channels | 9 |
| abstract_inverted_index.contains | 166 |
| abstract_inverted_index.entities | 106, 154 |
| abstract_inverted_index.extracts | 101 |
| abstract_inverted_index.involves | 7 |
| abstract_inverted_index.methods, | 94 |
| abstract_inverted_index.multiple | 8, 52 |
| abstract_inverted_index.proposed | 110, 134 |
| abstract_inverted_index.proposes | 35 |
| abstract_inverted_index.relation | 97, 115 |
| abstract_inverted_index.scenario | 2, 123 |
| abstract_inverted_index.triples, | 168 |
| abstract_inverted_index.assembly. | 127 |
| abstract_inverted_index.construct | 60 |
| abstract_inverted_index.excellent | 137 |
| abstract_inverted_index.knowledge | 14, 25, 64, 160, 171 |
| abstract_inverted_index.operation | 1 |
| abstract_inverted_index.presented | 174 |
| abstract_inverted_index.reasoning | 31 |
| abstract_inverted_index.validated | 119 |
| abstract_inverted_index.extraction | 46, 98, 116 |
| abstract_inverted_index.interface. | 179 |
| abstract_inverted_index.mechanical | 125 |
| abstract_inverted_index.relational | 45 |
| abstract_inverted_index.relations, | 156 |
| abstract_inverted_index.relations. | 108 |
| abstract_inverted_index.association | 103 |
| abstract_inverted_index.constructed | 159 |
| abstract_inverted_index.human-robot | 4, 73 |
| abstract_inverted_index.information | 104 |
| abstract_inverted_index.interactive | 178 |
| abstract_inverted_index.performance | 138 |
| abstract_inverted_index.recognition | 40, 81, 112 |
| abstract_inverted_index.traditional | 93 |
| abstract_inverted_index.experimental | 129 |
| abstract_inverted_index.segmentation | 91 |
| abstract_inverted_index.collaboration | 5 |
| abstract_inverted_index.collaborative | 20, 142 |
| abstract_inverted_index.heterogeneous | 53 |
| abstract_inverted_index.human-machine | 141 |
| abstract_inverted_index.respectively. | 157 |
| abstract_inverted_index.collaboration. | 74 |
| abstract_inverted_index.object> | 71 |
| abstract_inverted_index.Bert-BiGRU-ATT, | 48 |
| abstract_inverted_index.Bert-BiLSTM-CRF | 42 |
| abstract_inverted_index.<assembly | 70 |
| abstract_inverted_index.Chinese-character-based | 37, 78 |
| abstract_inverted_index.<operator> | 68 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.27282281 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |