Recognizing Core Knowledge From Domain Knowledge Network for Platform-Based Business Development

As an emerging topic in industrial digital transformation, digital business development in the context of platformization has received widespread attention. A large number of industrial companies have established new platform-based systems for digital business development by integrating their origin...

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Veröffentlicht in:IEEE transactions on computational social systems 2024-10, Vol.11 (5), p.6125-6134
Hauptverfasser: Sun, Poly Z.H., Jiang, Hongwei, Wang, Chengjun, Ru, Xinfeng, Ming, Xinguo
Format: Artikel
Sprache:eng
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Zusammenfassung:As an emerging topic in industrial digital transformation, digital business development in the context of platformization has received widespread attention. A large number of industrial companies have established new platform-based systems for digital business development by integrating their original information systems. The unified platform development mode promotes the integration of previously decentralized knowledge. However, the massive expansion of the knowledge system under platformization causes it to be no easier for developers to master or understand the core knowledge (context, concepts, and elements) of the business to be developed. According to the above dilemmas we have observed in the industry, in this article, a domain knowledge network modeling method for the knowledge system under platformization and a GP-based rule generation method for recognizing core business knowledge in the domain knowledge network are proposed for the first time. Our experiment and practical case study verify that our method can recognize a set of core business knowledge from a large knowledge network efficiently, which could help developers understand the business to be developed with a lower cognitive load. We hope the idea of platform-based business development and core business knowledge recognition can provide a reference for those companies that need efficient digital business development.
ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2024.3385671