Unified environment for fusion of information retrieval approaches

Abstract

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.