Active Information Gathering by Making Use of Existing Databases

抄録

With the development of computer techniques, active mining which is a combination of active information gathering, user-centered mining and active user reaction has played an important role in the success of mining novel knowledge from data. Active information gathering is the technique which aims at effectively searching relevant information and conducting preprocess required before the user-centered mining process. Even though there are a large number of researches concerning the problem of mining biomedical literature databases, the importance of active information gathering in such kinds of researches has not been mentioned so far. In this paper, we consider the problem of selecting the articles of experts' interest from a literature database by making use of existing databases and machine learning techniques. This problem could be considered as an active information gathering problem and is useful from the viewpoint of active mining prospect. The results show the effectiveness of making use of existing databases in terms of reducing the number of training data required for the learning system while maintaining the quality of the obtained documents.