Object Distinction Based on Decision Tree

In the problem of Object Distinction, different objects share identical names, retrieval one time will get many unrelated records and user cannot distinguish them easily. In this paper, we introduce a new method to distinguish objects with the same name, we first calculate the similarity values of the context attributes of the two objects with identical names, then we use these context attributes similarity values to build a decision tree model based on the training set. For the problem of object distinction for people, we combine the affiliation similarity with other context attributes similarity to judge whether the two people who share the same name correspond to the same people in real life. Experiments show that our method based on affiliation and Decision Tree can achieve high accuracy.