A rule based expert system generation framework using an open source business rule engine

Knowledge is key instrument for the deciding processes. On the other hand, for a deciding process, gathering knowledge and learning are very difficult phases. For this reason, in the last decades, studies are focused on the machine-learning systems and the expert systems for the most of the knowledge oriented areas, like academic, commercial, military and industrial areas. In this thesis, a framework is developed for the rule base learning expert systems. Briefly, this framework will take a data set, induct the rules from this data set, construct an expert system according to inducted rules, and give a web based interface for testing new cases. There are a lot of concepts in this study. Classification, decision tree, knowledge acquisition, ID3 algorithm, rule base systems, expert systems, rule engines, open source perspective are some of them. These concepts will be discussed briefly, after the discussion; framework will be explained with some examples. Examples will show the reusability of the framework. Different data set can be applied the framework. But data set must be convenient to the ID3 decision tree algorithm. Other restrictions will be defined next sections. After constructing expert system new cases can be tested. This framework has some principles: ? Java technologies are used ? Open source tools are used where needed ? Standardizations are applied where available As a result of these principles, usability of the framework is dramatically increased.