Knowledge reuse enhancement with motional visual representation
The growing complexity of information and documents has made it difficult for knowledge receivers to understand digital contents; therefore, multiple knowledge representation schemes are required for enterprise knowledge services. Traditional schemes for explicit knowledge representation within enterprise and academic circles are primarily text oriented, and thus, a great deal of time and effort is required for knowledge receivers to understand the contents, especially for motion knowledge. In order to enhance knowledge reuse with motion knowledge extraction, representation, and visualization, this research focuses on the development of a motion knowledge representation and visualization (MKRV) model for Chinese documents with three modules, namely, the automatic thesaurus definition (ATD) module, the target sentence extraction and formatting (TSEF) module, and the motion knowledge visualization (MKV) module. Moreover, based on the proposed model, a motion knowledge representation and management system (MKRMS) is established. A real-world case of computer assembly is also applied in order to verify the feasibility of the proposed model. The verification results show that the system could achieve a high-performance level with 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 contents of motion knowledge. The proposed model can be applied in enterprise e-training and knowledge management systems to enhance reuse of domain knowledge.
knowledge representation motion knowledge knowledge visualization knowledge management vrml