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ABSTRACT

In some retrieval situations, a system must search across multiple collections. This task, referred to as federated search, occurs for example when searching a distributed index or aggregating content for web search. Resource selection refers to the subtask of deciding, given a query, which collections to search. Most existing resource selection methods rely on evidence found in collection content. We present an approach to resource selection that combines multiple sources of evidence to inform the selection decision. We derive evidence from three different sources: collection documents, the topic of the query, and query click-through data. We combine this evidence by treating resource selection as a multiclass machine learning problem. Although machine learned approaches often require large amounts of manually generated training data, we present a method for using automatically generated training data. We make use of and compare against prior resource selection work and evaluate across three experimental testbeds.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms

Keywords:
distributed information retrieval, federated search, query classification, resource selection