Finding the most similar documents across multiple text databases

Finding the most similar documents across multiple text databases

We present a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, databases are ranked in a certain order. Next, documents are retrieved from the databases according to t...

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Hauptverfasser: Clement Yu, King-Lup Liu, Wensheng Wu, Weiyi Meng, Rishe, N.
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Sprache:eng
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Australia
Computer networks
Database systems
Indexing
Information retrieval
Information systems
Internet
ISDN
Machine learning
Transaction databases
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creator Clement Yu
King-Lup Liu
Wensheng Wu
Weiyi Meng
Rishe, N.
description We present a methodology for finding the n most similar documents across multiple text databases for any given query and for any positive integer n. This methodology consists of two steps. First, databases are ranked in a certain order. Next, documents are retrieved from the databases according to the order and in a particular way. If the databases containing the n most similar documents for a given query can be ranked ahead of other databases, the methodology will guarantee the retrieval of the n most similar documents for the query. A statistical method is provided to identify databases, each of which is estimated to contain at least one of the n most similar documents. Then, a number of strategies are presented to retrieve documents from the identified databases. Experimental results are given to illustrate the relative performance of different strategies.
doi_str_mv 10.1109/ADL.1999.777710
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Australia
Computer networks
Database systems
Indexing
Information retrieval
Information systems
Internet
ISDN
Machine learning
Transaction databases
title Finding the most similar documents across multiple text databases
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