Simple adaptations of data fusion algorithms for source selection

Abstract

Source selection deals with the problem of selecting the most appropriate information sources from the set of, usually non-intersecting, available document collections. On the other hand, data fusion techniques (also known as metasearch techniques) deal with the problem of aggregating the results from multiple, usually completely or partly intersecting, document sources in order to provide a wider coverage and a more effective retrieval result. In this paper we study some simple adaptations to traditional data fusion algorithms for the task of source selection in uncooperative distributed information retrieval environments. The experiments demonstrate that the performance of data fusion techniques at source selection tasks is comparable with that of state-of-the-art source selection algorithms and they are often able to surpass them. © Springer-Verlag Berlin Heidelberg 2009.