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Developing an ontology-based knowledge combination mechanism to customise complementary knowledge content

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In rapidly changing business environments, enterprises are encountering increasingly complicated and multidimensional challenges related to R&D and manufacturing processes. To address these challenges, knowledge requesters working for these enterprises must effectively gain knowledge from enterprise knowledge bases, other enterprises or knowledge markets. However, knowledge requesters cannot obtain a desired and distinctive solution from a single knowledge source, including their own enterprise knowledge base. If knowledge can be customised by combining knowledge from various sources to create personalised complementary knowledge combinations that are more suited to their knowledge requirements, then knowledge acquisition and searches invariably become more efficient and accurate. Therefore, an ontology-based complementary knowledge combination mechanism, which can be employed to enhance online digitised knowledge recommendations or enterprise knowledge management systems, was developed in this study. First, a knowledge requirement model and a knowledge-product ontology model was constructed to describe and structure knowledge content, and then an ontology similarity calculation method was developed to enable precise comparisons of the requirements and knowledge structuralised by the knowledge requirement and product models. Finally, according to the four indicators of similarity, duplication, amount of knowledge and cost, a genetic algorithm (GA)-based knowledge-product ontology combination method was developed to identify optimal knowledge combinations and subsequently provide a reference for knowledge requesters.
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Keywords: GA; knowledge combination; knowledge creation; knowledge market; ontology; recommendation

Document Type: Research Article

Affiliations: 1: Department of Electronic Commerce Management, Nanhua University, Chiayi, Taiwan (R.O.C) 2: Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, Taiwan (R.O.C)

Publication date: May 4, 2015

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