2017•ACM SIGIR ForumRequires access

Searching Distributed Collections With Inference Networks

James P. Callan, Zhihong Lu, W. Bruce Croft

Open publisher page 611 citations

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.

About this research paper

What this paper is about

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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OpenAlex reports 611 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Computer science, Ranking (information retrieval), Relevance (law), Information retrieval, Inference, Set (abstract data type), Data science, Data mining

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