Probabilistic Modeling of Distributed Information Retrieval

This paper describes a model for optimum information retrieval over a distributed document collection. The model stems from Robertson's Probability Ranking Principle: Having computed individual document rankings correlated to diierent subcollections, these local rankings are stepwise merged into a nal ranking list where the documents are ordered according to their probability of relevance. Here, a full dissemination of subcollection-wide information is not required. The documents of diierent subcollec-tions are assumed to be indexed using diierent indexing vocabularies. Moreover, local rankings may be computed by individual probabilistic retrieval methods. The underlying data volume is arbitrarily scalable. A criterion for eeectively limiting the ranking process to a subset of subcollections extends the model.