Research on topic map learning for domain knowledge
Huang Guo · Jisuanji gongcheng yu sheji · 2007
In order to solve the problem of much time costing in traditional topic map constructing for domain knowledge, a method of topic map learning for domain knowledge (TMLDK) is put forward, and the main steps of TMLDK is analyzed as well as its activity flow is built. And then the key technology has been discussed on domain Keywords extracting, conversion from the keyword to topic and information integration. After that, the courseware is converted to TMC half-automatically through the technology of shallow parsing and similarity computing. Finally the prototype of TMLDK is developed by the use TM4J and Java, which verifies the model. TMLDK is able to help the expert to build topic map for the domain knowledge, which can save plenty of manpower and time, and moreover, is convenient for upper maintenance and update of TMC.