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Abstract
En 中文
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.
Keywords:
DISTRIBUTED INFORMATION-RETRIEVAL
DECISION-THEORETIC APPROACH
RESULT MERGING STRATEGIES
COLLECTION SELECTION
DATABASE SELECTION
DATA FUSION
PERFORMANCE
OVERLAP
SYSTEM
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Journal
F
IF:
12.9
Papers:
50
Citations:
824
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