Abstract
Natural language is used to represent human thoughts and human actions. Business rules described by natural language are very hard for machine to understand. In order to let machine know the business rules, parts of business process, we need to translate them into a language which machine can understand. Object constraint language is one of those languages. In this paper we present a statistical machine learning method to understand the natural business rules and then translate them into object constraint language. Subsequently a translation algorithm for business process modeling is also provided. A real case, air cargo load planning process is proposed to illustrate the efficiency and effective of the method and the algorithm. The result has shown that this method and algorithm enrich business process modeling technology and enhance the efficiency of software developers in business process modeling.
