Injection Molding Knowledge Graph Based on Ontology Guidance and its Application to Quality Diagnosis

WANG Yalin; ZOU Jiangfeng; WANG Kai; YUAN Xiaofeng; XIE Shengli

doi:10.11999/JEIT211416

Volume 44 Issue 5

May 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1521-1529

WANG Yalin, ZOU Jiangfeng, WANG Kai, YUAN Xiaofeng, XIE Shengli. Injection Molding Knowledge Graph Based on Ontology Guidance and its Application to Quality Diagnosis[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1521-1529. doi: 10.11999/JEIT211416

Citation:

WANG Yalin, ZOU Jiangfeng, WANG Kai, YUAN Xiaofeng, XIE Shengli. Injection Molding Knowledge Graph Based on Ontology Guidance and its Application to Quality Diagnosis[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1521-1529. doi: 10.11999/JEIT211416

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Injection Molding Knowledge Graph Based on Ontology Guidance and its Application to Quality Diagnosis

doi: 10.11999/JEIT211416

1.
School of Automation, Central South University, Changsha 410083, China
2.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China (U1911401), The National Key Research and Development Program of China (2020YFB1713800), The Science and Technology Innovation Program of Hunan Province in China (2021RC4054), The Fundamental Research Funds for the Central Universities of Central South University (2021zzts0711)

Received Date: 2021-12-02
Rev Recd Date: 2022-04-14

Available Online: 2022-04-24

Publish Date: 2022-05-25

Abstract

Abstract

Due to the lack of mature labeled corpus and large-scale injection molding knowledge graphs for defection diagnosis, industrial knowledge graphs are constructed with high cost and low quality. A framework for constructing industrial knowledge graph based on ontology guidance is developed in this paper. Firstly, the injection molding ontology guided by defect-appearance-cause-scheme chain is designed to limit the collection of web pages. Then, the ontology information is sequentially integrated into the trigger thesaurus to improve the knowledge extraction performance of unstructured web text. Finally, the two-level entity merging method is carried out by combining with the attribute similarity in ontology, which realized the fusion of redundant knowledge. Compared with the existing methods, the accuracy of domain knowledge is higher than 95%, which can be used for tracing the defect quickly.
- Injection molding,
- Knowledge graph,
- Ontology engineering,
- Entity merging

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References(29)

References

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