A Machine Understandable Dictionary For Automatic Discovery Of Programming Knowledge Resources

Nowadays, the online programming systems can't provide the problems to the user by analyzing the knowledge of the user. So the user has to find the right problem to solve from thousands of the problems. And when the user meets some difficulty during their solving the problem, he can't get any accurate help from the system. If the system can provide user suitable problems and accurate help, it will save user a lot of time and become more useful. However, if the machine wants to have such kind of analysis ability, she needs to know the user's knowledge, the knowledge involved during solving the problem and programming domain knowledge. And at first the machine needs to understand the programming domain knowledge, then, it can get the user's knowledge by analyzing the codes he written and get the knowledge involved during solving the problem by analyzing the correct codes of the problem. And if you want to make the machine understand the programming domain knowledge, you should describe all programming knowledge which appears in the intelligent system in a machine understand language. This paper studies the machine understandable dictionary for automatic discovery of programming knowledge resources. The dictionary includes two parts which are the programming domain knowledge ontology and the services which dictionary provides to the subsystems in the whole tutoring system. When building the ontology model, at first, we need to collect the information. Second, build the concept model of the domain knowledge. Third, transform the concept model into the ontology model. During the building of the ontology model, the method of getting knowledge is very important and programming domain knowledge is very special. If only use the way from knowledge engineering, the data can't meet the system requirement. So we need to add something new into the method. After the knowledge getting, we should analysis the knowledge, then get the concept model and transform it into the ontology model. Finally, we add the instants into the ontology. After this, we will open some service toward the subsystems in the on-line tutoring system. Then we finished the whole dictionary. The dictionary can really meet the requirements from the subsystems and is a very important part of the whole intelligent tutoring system. The dictionary makes the intelligence of the whole system become possible.