Abstract
Due to the rapid growth of information technology, digital information has significantly increased over the Internet. The growing complexity of information and documents has made it hard for knowledge receivers to efficiently and accurately to recognize the digital contents. Therefore, an appropriate knowledge representation scheme is required for enterprise knowledge management and services. Traditional schemes for explicit knowledge representation within the enterprise and academic circles are mostly text-oriented and as a result, much time and efforts are required for knowledge receivers to recognize the knowledge contents, especially for the motion knowledge. In this research, a three-phase methodology (including automatic thesaurus definition (ATD), target sentence extraction and formatting (TSEF) and motion knowledge visualization (MKV)) for motion knowledge extraction, representation and visualization is developed. Moreover, based on the proposed methodology, a Motion Knowledge Representation and Management System (MKRMS) is established, and a “Computer Assembly” case is applied in order to verify the feasibility of the proposed model. The verification results show that the system could achieve a well performance with simply a small amount of training data. As a whole, this research provides a knowledge representation and visualization approach to facilitate knowledge receivers to efficiently and accurately acquire the knowledge contents. The proposed methodology can be applied in enterprise e-training and knowledge management systems to enhance reuse of domain knowledge.