Federated search (federated information retrieval or distributed information

retrieval) is a technique for searching multiple text collections

simultaneously. Queries are submitted to a subset of collections that

are most likely to return relevant answers. The results returned by

selected collections are integrated and merged into a single list. Federated

search is preferred over centralized search alternatives in many

environments. For example, commercial search engines such as Google

cannot easily index uncrawlable hidden web collections while federated

search systems can search the contents of hidden web collections

without crawling. In enterprise environments, where each organization

maintains an independent search engine, federated search techniques

can provide parallel search over multiple collections.

There are three major challenges in federated search. For each query,

a subset of collections that are most likely to return relevant documents

are selected. This creates the collection selection problem. To

be able to select suitable collections, federated search systems need to

acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user.

This final step is the result merging problem.

The goal of this work, is to provide a comprehensive summary of the

previous research on the federated search challenges described above.


Publication typeBook
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