A MODEL FOR TEMPORAL KNOWLEDGE VISUALIZATION

In order to assist knowledge receivers to efficiently and accurately understand the useful knowledge and information from a large number of digital documents, knowledge representation mechanism has become an important issue that the content providers should concern. In the traditional knowledge sharing environment, the abstract temporal knowledge such as temporal information or temporal relationship of events is hard to be fully recognized by knowledge receivers in a short time based on the text-oriented knowledge representation schemes. Therefore, a visual representation scheme for the text-oriented temporal knowledge will support knowledge receivers to efficiently recognize this type of knowledge. The core idea of this research is to extract and tag the temporal terms from text-oriented documents via temporal knowledge analysis and tagging procedure. As a result, the temporal relationships of events can be derived from the temporal information and the corresponding visualized display of temporal knowledge can also be established according to the extracted temporal information and events. This research develops a methodology for temporal knowledge visualization so that computer systems can automatically convert the text-oriented temporal knowledge into visualized display. A methodology for temporal information extraction and temporal relationship analysis is developed. Furthermore, a Web-based temporal knowledge extraction and representation system (TKERS) is also developed to demonstrate the feasibility of the proposed methodology. As a whole, this research provides an effective visualization methodology for the knowledge providers and receivers to improve efficiency for temporal knowledge recognition. The methodology can be further applied in many enterprise activities such as e-training or knowledge management to enhance reuse of temporal knowledge.