A Hybrid Object Matching Method for Deep Web Information Integration

Object matching is a crucial step to integration of Deep Web sources. Existing methods suppose that record extraction and attribute segmentation are of high accuracy. But because of limitation of extraction techniques, information gained through the above methods is often incomplete. If we match object base on noisy and incomplete information, we can not achieve satisfactory performance. This paper proposes a hybrid object matching method, which considers structured and unstructured features and multi-level errors in extraction. We compare performance of the unstructured, structured and hybrid object matching models in our prototype system, which indicates that hybrid method has the highest performance.