Summary: A Unified Environment for Fusion of Information Retrieval Approaches
M. Catherine McCabe Abdur Chowdhury
George Mason University IIT Research Inst.
David A. Grossman Ophir Frieder
US Government Illinois Inst. of Tech
Prior work has shown that combining results of various retrieval approaches and query representations
can improve search effectiveness. Today, many meta-search engines exist which combine the results of
various search engines in the hopes of improving overall effectiveness. However, the combination of
results from different search engines masks variations in parsers, and other indexing techniques
(stemming, stop words, etc.) This makes it difficult to assess the utility of the fusion technique. We have
implemented the two most prevalent retrieval strategies: probabilistic and vector space using the same
parser and the same relational retrieval engine. First, we identified a model that enables the fusion of an
arbitrary number of sources. Next, we tested various linear combinations of these two methods as well as
various thresholds for identifying retrieved documents. Our results show some improvement of
effectiveness, but they also provide us for a baseline from which we can continue with other retrieval
strategies and test the effect of fusing these strategies.