Traditional search engines like Google typically ignore a large amount of valuable information that is ``hidden'' behind the search engines of many online text information sources. Federated text search provides one-stop access to the hidden information via a single interface that connects to multiple search engines of text information sources. Existing federated search solutions only focus on content relevance and ignore a large amount of valuable information about users and information sources.
This project includes research on: (1) Multiple Type Resource Representation: model important information of text information sources such as search response time and search engine effectiveness; (2) Utility-Centric Resource Selection: satisfy a user's search criteria by considering multiple types of evidence such as content relevance, search results from past queries, personal information needs, and search response time; (3) Effective and Efficient Results Merging: produce accurate merged ranked results with little cost of acquiring the content information of the returned documents from selected information sources; (4) System Adaptation by Results Analysis: analyze the search results from past queries for more accurate resource representation, resource selection and results merging; (5) System Development and Evaluation: build and test algorithms within research environments as well as a new FedLemur system for a real world application. The project advances the state-of-the-art of research in federated search and serves as a bridge from turning research topics into practical applications. The education component of the project will expand information retrieval instruction to address multi-disciplinary requirements and improve the education of information technology workforce.
Last modified: Wednesday, May 29, 2013 10:24 AM