{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81746"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81746","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Integrating Deep Web Data Sources","abstract":"This dissertation presents IceQ, a novel and effective interface integration system. In developing IceQ, we address the limitations of existing solutions and make several key contributions. First, we propose a hierarchical modeling of interfaces and develop a novel spatial clustering algorithm to extract the hierarchical schema of query interface. Second, we develop a novel interactive clustering-based matching algorithm to accurately match a large number of schemas and effectively resolve uncertain mappings via user interaction. 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Third, we develop a question-answering technique to learn attribute instances from the Web to assist in schema matching. Fourth, we propose a novel constraint-based optimization framework for merging schemas and develop an effective merging algorithm based on the idea of clustering aggregation. 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