Robust Result Merging Using Sample-Based Score Estimates

In federated information retrieval, a query is routed to multiple collections and a single answer list is constructed by combining the results. Such metasearch provides a mechanism for locating documents on the hidden Web and, by use of sampling, can proceed even when the collections are uncooperative. However, the similarity scores for documents returned from different collections are not comparable, and, in uncooperative environments, document scores are unlikely to be reported. We introduce a new merging method for uncooperative environments, in which similarity scores for the sampled documents held for each collection are used to estimate global scores for the documents returned per query. This method requires no assumptions about properties such as the retrieval models used. Using experiments on a wide range of collections, we show that in many cases our merging methods are significantly more effective than previous techniques.