Automatic repairing of Web wrappers by combining redundant views

Abstract

We address the problem of automatic maintenance of Web wrappers used in data integration systems to encapsulate an access to Web information providers. The maintenance of Web wrappers is critical as providers often changes the page format and/or structure making wrappers inoperable. The solution we propose extends the conventional wrapper architecture with a novel component of automatic maintenance and recovery. We consider the automatic recovery as special type of the classification problem and use ensemble methods of machine learning to build alternative views of provider pages. We combine extraction rules of conventional wrappers with content features of extracted information to accurate recovery from three types of format changes, namely, content, context and structural changes. We report results of the recovery performance for format changes at widely used Web providers.