A Study On Feature Extraction In Money-laundering Cases Based On Ontology

The feature of money-laundering case is a important reference which could judge the activations of money-laundering in Financial fields. In the test of money-laundering with CBR(Case Based Reasoning), the first task is to input the features into the cases database. As money-laundering case reports themselves with the properties of information hidden, it makes this work is still at the stage of manual, the both of efficiency and accuracy are not Satisfactory. According to this ,the paper presents a method which is based on ontology. Since the introduction of Domain Ontology . it implemented the full text of knowledge acquisition and make people get out from the heavy manual labor.As a conceptualization of the explicit note ,Ontology is the description of conceptions and relations which is in the objective world. In the actual study , the construction of ontology is usually with the help of experts in the field . We analysis a large number of money-laundering cases reports and abstract the concepts into a model. Extract the keyword as the classes in the ontology , then definite subclasses and attribution relations of all kinds of classes. At last, it assignments all the instances for the classes which the instances are certain and add the restrictions between all the classes.In the knowledge acquisition , this paper uses a methods of pattern matching and text definition .The task of pattern matching is the certainty of position which index keyword in the text vector. The production rules and data rules of text definition constraint the formation of extracted information . In addition ,we make a deep study for the algorithm of pattern matching and improve the present algorithm.At last , the issues designed a simulated system, the system is developed with Java and is running the mode of B/S. In the process of developing , we use the open-source toolkit protégéto edit the Ontology and use Jena to apply the Ontology .The data for the input of experiment is from the samples of money-laundering case reports which supplied by the official agency, and the data of output is the structured data in the Database.