Research on Method for Collaborative Acquisition of Expertise Domain Knowledge by Multiple People

Addressing the problems of complex forms, low structurization and insufficient reliability of automatic acquisition of professional knowledge sources in the manufacturing industry, this paper proposes an improved multi-person collaborative knowledge acquisition method for professional fields. Drawing on the quality control concept of “three reviews and three proofs” in the publishing industry and combining the characteristics of professional knowledge acquisition tasks, this method constructs a knowledge acquisition process with the collaborative participation of editors and professionals. This paper designs a quality assurance mechanism from three dimensions, namely personnel quality, process quality and result quality; introduces triangular fuzzy numbers to evaluate personnel quality; and establishes a process quality control model under multi-level inspection. Taking the knowledge acquisition project of CNC Machining Manual as an example, 39 professionals completed large-scale professional knowledge processing tasks within 60 working days. Compared with the traditional manual knowledge acquisition method, under similar workload conditions, the proposed method reduces the task completion time by approximately 40% and improves knowledge quality by approximately 10%. The research results show that this method can enhance the organization, inspectability and result stability of the complex professional knowledge acquisition process, and is suitable for constructing vertical domain knowledge bases with high quality requirements.

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Publication Details

Journal
Processes
Published
2026-06-25
DOI
https://doi.org/10.3390/pr14132074
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on Method for Collaborative Acquisition of Expertise Domain Knowledge by Multiple People

Ziyan Zhu, Yan Cao, Leijie Fu, Hu Qiao et al.
Processes
Manufacturing Process and Optimization
article

Research on Method for Collaborative Acquisition of Expertise Domain Knowledge by Multiple People

Ziyan Zhu, Yan Cao, Leijie Fu, Hu Qiao, Zekai Peng, Yv Bai
article en

Abstract

Addressing the problems of complex forms, low structurization and insufficient reliability of automatic acquisition of professional knowledge sources in the manufacturing industry, this paper proposes an improved multi-person collaborative knowledge acquisition method for professional fields. Drawing on the quality control concept of “three reviews and three proofs” in the publishing industry and combining the characteristics of professional knowledge acquisition tasks, this method constructs a knowledge acquisition process with the collaborative participation of editors and professionals. This paper designs a quality assurance mechanism from three dimensions, namely personnel quality, process quality and result quality; introduces triangular fuzzy numbers to evaluate personnel quality; and establishes a process quality control model under multi-level inspection. Taking the knowledge acquisition project of CNC Machining Manual as an example, 39 professionals completed large-scale professional knowledge processing tasks within 60 working days. Compared with the traditional manual knowledge acquisition method, under similar workload conditions, the proposed method reduces the task completion time by approximately 40% and improves knowledge quality by approximately 10%. The research results show that this method can enhance the organization, inspectability and result stability of the complex professional knowledge acquisition process, and is suitable for constructing vertical domain knowledge bases with high quality requirements.

ProcessesVol. 14(13)
Peter the Great St. Petersburg Polytechnic University (RU), Xi'an Polytechnic University (CN), Xi'an Technological University (CN)
Key Research and Development Projects of Shaanxi Province
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
Manufacturing Process and Optimization
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