TreeCluster: Clustering results of keyword search over databases

Peng, Z; Zhang, J; Wang, S; Qin, L

TreeCluster: Clustering results of keyword search over databases

Peng, Z Zhang, J Wang, S Qin, L

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Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4016 LNCS pp. 385 - 396
Issue Date:: 2006-01-01

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Field	Value	Language
dc.contributor.author	Peng, Z	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062	en_US
dc.date.issued	2006-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4016 LNCS pp. 385 - 396	en_US
dc.identifier.isbn	9783540352259	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28280
dc.description.abstract	A critical challenge in keyword search over relational databases (KSORD) is to improve its result presentation to facilitate users' quick browsing through search results. An effective method is to organize the results into clusters. However, traditional clustering method is not applicable to KSORD search results. In this paper, we propose a novel clustering method named TreeCluster. In the first step, we use labels to represent schema information of each result tree and reformulate the clustering problem as a problem of judging whether labeled trees are isomorphic. In the second step, we rank user keywords according to their frequencies in databases, and further partition the large clusters based on keyword nodes. Furthermore, we give each cluster a readable description, and present the description and each result graphically to help users understand the results more easily. Experimental results verify our method's effectiveness and efficiency. © Springer-Verlag Berlin Heidelberg 2006.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	TreeCluster: Clustering results of keyword search over databases	en_US
dc.type	Journal Article
utslib.citation.volume	4016 LNCS	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Hong Kong	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	4016 LNCS	en_US

Abstract:

A critical challenge in keyword search over relational databases (KSORD) is to improve its result presentation to facilitate users' quick browsing through search results. An effective method is to organize the results into clusters. However, traditional clustering method is not applicable to KSORD search results. In this paper, we propose a novel clustering method named TreeCluster. In the first step, we use labels to represent schema information of each result tree and reformulate the clustering problem as a problem of judging whether labeled trees are isomorphic. In the second step, we rank user keywords according to their frequencies in databases, and further partition the large clusters based on keyword nodes. Furthermore, we give each cluster a readable description, and present the description and each result graphically to help users understand the results more easily. Experimental results verify our method's effectiveness and efficiency. © Springer-Verlag Berlin Heidelberg 2006.

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http://hdl.handle.net/10453/28280