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Manufacturing Technology Knowledge Retrieval System Based on OSE and UCF

Authors :
Linyan Liu
Tangxiao Yuan
Haojie Song
Huifen Wang
Source :
IEEE Access, Vol 7, Pp 39623-39638 (2019)
Publication Year :
2019
Publisher :
IEEE, 2019.

Abstract

Nowadays it is commonly accepted that knowledge, as a resource has become increasingly important in the drive toward knowledge-driven innovation in the manufacturing industry, and which is especially critical for the research and development of complex equipment. In China, there are several fields of manufacturing, such as aviation, ship, and electronics. Although each of them has its own professional technology, they also take advantage of the basic common manufacturing technology. Thus, it is meant for sharing manufacturing technology knowledge. In order to promote innovation, a manufacturing technology knowledge retrieval system is developed to make more efficient use of manufacturing technology knowledge. Manufacturing technology knowledge is a phrase with vast meanings, which may include knowledge of different manufacturing process means, machine, and process capabilities or the latest technology developments. In this system, the manufacturing technology knowledge is defined and organized. A knowledge retrieval method based on the weighted fusion of ontology-based semantic extension (OSE) and user-based collaborative filtering (UCF) is proposed and described detailed. On this basis, the prototype system of manufacturing technology knowledge retrieval system is developed. Its architecture and main functions are elaborated. The results of this system facilitate the sharing of manufacturing knowledge, and it is expected to accelerate the process of knowledge transfer, to improve the level of the overall manufacturing technology.

Details

Language :
English
ISSN :
21693536
Volume :
7
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.07369630bb6041aa88dbe1f7d14f3c51
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2019.2906422