Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/160698
Title: | A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design | Authors: | Li, Xinyu Chen, Chun-Hsien Zheng, Pai Jiang, Zuhua Wang, Linke |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2021 | Source: | Li, X., Chen, C., Zheng, P., Jiang, Z. & Wang, L. (2021). A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design. Knowledge-Based Systems, 215, 106739-. https://dx.doi.org/10.1016/j.knosys.2021.106739 | Project: | RCA-16/434 SCO-RP1 |
Journal: | Knowledge-Based Systems | Abstract: | To proactively fulfill multiple stakeholders’ needs in the engineering solution design process, the knowledge recommendation approach is adopted as a key element in the knowledge management system. Nevertheless, most existing knowledge recommendation approaches cannot simultaneously meet the higher standard of in-context accuracy and diversity. To address the issue, this paper proposes a context-aware diversity-oriented knowledge recommendation approach, thereby assisting stakeholders to accomplish engineering solution design in a smarter manner. Three diversity concerns, namely item-diversity, context-diversity, and user-diversity are addressed by semantic-based content analysis, context definition and awareness, and user profile modeling, respectively. Hence, the proposed approach not only maximizes the diversity of the recommended knowledge but also guarantees its accuracy under multiple problem-solving contexts. Moreover, a practical engineering solution design case on a Smart 3D printer platform is conducted, to validate the efficacy of the proposed approach in providing usable and diverse knowledge items. It is anticipated this work can provide useful insights to practitioners in their knowledge-based engineering solution design process. | URI: | https://hdl.handle.net/10356/160698 | ISSN: | 0950-7051 | DOI: | 10.1016/j.knosys.2021.106739 | Schools: | School of Electrical and Electronic Engineering School of Mechanical and Aerospace Engineering |
Rights: | © 2021 Elsevier B.V. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Journal Articles MAE Journal Articles |
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