Integrating Deep Web Data Sources

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. 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. Extensive experiments have been conducted to evaluate IceQ and the results show that it is highly effective.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Wu, Wensheng

Contributors dc:contributor

• Doan, AnHai
• Clement Yu

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3243027

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81746