C4.5: programs for machine learning
C4.5: programs for machine learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Machine Learning
Query Formulation from High-Level Concepts for Relational Databases
UIDIS '99 Proceedings of the 1999 User Interfaces to Data Intensive Systems
DBXplorer: A System for Keyword-Based Search over Relational Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A Probabilistic Approach to Metasearching with Adaptive Probing
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Understanding Web query interfaces: best-effort parsing with hidden syntax
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
An ontology-based semantic tagger for IE system
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Distributed search over the hidden web: hierarchical database sampling and selection
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
BANKS: browsing and keyword searching in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Many Web sources provide forms to allow users to query their hidden data. For instance, online stores such as Amazon.com have search interfaces, using which users can query information about books by providing conditions on attributes of title, author, and publisher. We propose a novel system framework that supports keyword queries on structured data behind such limited search forms. It provides user-friendly query interfaces for users to type in IR-style keyword queries to find relevant records. We study research challenges in the framework and conduct extensive experiments on real datasets to show the practicality of our framework and evaluate different algorithms.