WIDIT: Fusion-Based Approach to Web Search Optimization

Abstract

To facilitate both the understanding and the discovery of information, we need to utilize multiple sources of evidence, integrate a variety of methodologies, and combine human capabilities with those of the machine. The Web Information Discovery Integrated Tool (WIDIT) Laboratory at the School of Library and Information Science, Indiana University-Bloomington, houses several projects that employ this idea of multi-level fusion in the areas of information retrieval and knowledge discovery. This paper describes a Web search optimization study by the TREC research group of WIDIT, who explores a fusion-based approach to enhancing retrieval performance on the Web. In the study, we employed both static and dynamic tuning methods to optimize the fusion formula that combines multiple sources of evidence. By static tuning, we refer to the typical stepwise tuning of system parameters based on training data. “Dynamic tuning”, the key idea of which is to combine the human intelligence, especially pattern recognition ability, with the computational power of the machine, involves an interactive system tuning process that facilitates fine-tuning of the system parameters based on the cognitive analysis of immediate system feedback. The rest of the paper is organized as follows. The next section discusses related work in Web information retrieval (IR). Section 3 details the WIDIT approach to Web IR, followed by the description of our experiment using the TREC .gov data in section 4 and the discussion of results in section 5.