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Answering Top-k Keyword Queries on Relational Databases
http://hdl.handle.net/20.500.12678/0000005401
http://hdl.handle.net/20.500.12678/0000005401702b8fe0-ad8f-4f58-b58b-8216d3f9222a
57790874-522c-42cd-8d20-8a81173a74cd
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Answering Top-k Keyword Queries on Relational Databases.pdf (643 Kb)
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Journal article | ||||||
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Publication | ||||||
Title | ||||||
Title | Answering Top-k Keyword Queries on Relational Databases | |||||
Language | en | |||||
Publication date | 2012-07-01 | |||||
Authors | ||||||
Myint Myint Thein | ||||||
Mie Mie Su Thwin | ||||||
Description | ||||||
Keyword search in relational databases allows the user to search information without knowing database schema and using structural query language. As results needed by user are assembled from connected tuples of multiple relations, ranking keyword queries are needed to retrieve relevant results. For a given keyword query, we first generate candidate networks and also produce connected tuple trees according to the generated candidate networks by reducing the size of intermediate joining results. We then model the generated connected tuple trees as a document and evaluate score for each document to estimate its relevance. Finally, we retrieve top-k keyword queries by ranking the results. In this paper, we propose a new ranking method based on virtual document. We also propose Top-k CTT algorithm by using the frequency threshold value. The experimental results are shown by comparison of the proposed ranking method and the previous ranking methods on IMDB and DBLP datasets. | ||||||
Keywords | ||||||
Candidate Network, Connected Tuple Tree, Top-k, Keyword Query, Keyword Search, Relational Databases | ||||||
Journal articles | ||||||
IJIRR |