Meaningful labeling of integrated query interfaces
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Learning to extract form labels
Proceedings of the VLDB Endowment
Site-Wide Wrapper Induction for Life Science Deep Web Databases
DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
Deriving Customized Integrated Web Query Interfaces
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Labeling data extracted from the web
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
Parsing query interfaces of deep web: from specialization to generalization
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Web database schema identification through simple query interface
RED'09 Proceedings of the 2nd international conference on Resource discovery
Instance discovery and schema matching with applications to biological deep web data integration
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
A query interface matching approach based on extended evidence theory for deep web
Journal of Computer Science and Technology
Deep web integrated systems: current achievements and open issues
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Associating labels and elements of deep web query interface based on DOM
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
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Integrating Deep Web sources requires highly accurate semantic matches between the attributes of the source query interfaces. These matches are usually established by comparing the similarities of the attributes' labels and instances. However, attributes on query interfaces often have no or very few data instances. The pervasive lack of instances seriously reduces the accuracy of current matching techniques. To address this problem, we describe WebIQ, a solution that learns from both the Surface Web and the Deep Web to automatically discover instances for interface attributes. WebIQ extends question answering techniques commonly used in the AI community for this purpose. We describe how to incorporate WebIQ into current interface matching systems. Extensive experiments over five realworld domains show the utility ofWebIQ. In particular, the results show that acquired instances help improve matching accuracy from 89.5% F-1 to 97.5%, at only a modest runtime overhead.