Research on Non-interactive Literature-Based Knowledge Discovery

Knowledge discovery from non-interactive literature is a new method in informatics, which is useful to efficient usage of literature and discovery of implicit knowledge. In order to introduce the method into the research of the traditional Chinese medicine, using natural language processing (NLP) technique and method, this paper focuses on the research of data base required by Chinese non-interactive literature-based knowledge discovery. Based on the above research, it constructs a pertinence stop list and develops an subsidiary system of Chinese non-interactive literature-based knowledge discovery. Using some techniques and approaches such as semantic Web, explicit relation exclusion, semantic constraint, frequency filtering,etc. the system intelligently chooses the relations among non-interactive literatures. It simulates the process of non-interactive literature-based knowledge discovery successfully, aiming at the anticipative target to help scientific researchers to disclose implicit relation in Chinese literatures and introduce them to achieve knowledge discovery.