A Geometric probabilistic framework for data fusion in information retrieval

Abstract

Data fusion in information retrieval has been investigated by many researchers and quite a few data fusion methods have been proposed, but why data fusion can bring improvement in effectiveness is still not very clear. In this paper, we use a geometric probabilistic framework to formally describe data fusion, in which each component result returned from an information retrieval system for a given query is represented as a point in a multiple dimensional space. Then all the component results and data fusion results can be explained using geometrical principles. In such a framework, it becomes clear why quite often data fusion can bring improvement in effectiveness and accordingly what the favourable conditions are for data fusion algorithms to achieve better results. The framework can be used as a guideline to make data fusion techniques be used more effectively.