Machine learning and wrappers to the rescue of the WWW

Abstract

With huge amounts of information connected to the Internet, efficient and effective discovery of resource and C> knowledge using the Internet has become an imminent research issue. This rapid growth in data volume, user base, and data diversity render Internet-accessible information increasingly difficult to be sued effectively. Therefore, search for specific information on this massive and exploding Internet information resource base becomes highly critical. In this paper we discuss the issues involved in the application of machine learning techniques and wrappers to the problem of Internet-based information overload. We present a general architecture and describe how it has been instantiated in a functional system we developed. We employ a number of machine learning techniques together with automatically generated wrappers in an attempt to subdue the problem. Some performance tests are presented together with a discussion of the results.