Searching Distributed Collections With Inference Networks
James P. Callan, Zhihong Lu, W. Bruce Croft
Abstract
James P. Callan, Zhihong Lu, W. Bruce Croft
Abstract
The use of information retrieval systems in networked environments raises a new set of issues that have received little attention. These issues include ranking document collections for relevance to a query, selecting the best set of collections from a ranked list, and merging the document rankings that are returned from a set of collections. This paper describes methods of addressing each issue in the inference network model, discusses their implementation in the INQUERY system, and presents experimental results demonstrating their effectiveness.
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The use of information retrieval systems in networked environments raises a new set of issues that have received little attention. These issues include ranking document collections for relevance to a query, selecting the best set of collections from a ranked list, and merging the document rankings that are returned from a set of collections. This paper describes methods of addressing each issue in the inference network model, discusses their implementation in the INQUERY system, and presents experimental results demonstrating their effectiveness.
Key concepts: Computer science, Ranking (information retrieval), Relevance (law), Information retrieval, Inference, Set (abstract data type), Data science, Data mining