Collaborative querying through a hybrid query clustering approach

Lin Fu; Dion Hoe Lian Goh; Schubert Shou Boon Foo; Jin Cheon Na

doi:10.1007/978-3-540-24594-0_10

Collaborative querying through a hybrid query clustering approach

Lin Fu^*, Dion Hoe Lian Goh, Schubert Shou Boon Foo, Jin Cheon Na

^*Corresponding author for this work

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Chapter

27 Citations (Scopus)

Abstract

Harnessing previously issued queries to facilitate collaborative querying is an approach that can help users in digital libraries and other information systems better meet their information needs. Here, the kernel step is to identify and cluster similar queries by mining the query logs. However because of the short lengths of queries, it is relatively difficult to cluster queries effectively using on the terms used since they cannot convey enough information. This paper introduces a hybrid method to cluster queries by utilizing both the query terms and the results returned to queries. Experiments show that this method outperforms existing query clustering techniques.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Tengku Mohd Tengku Sembok, Halimah Badioze Zaman, Hsinchun Chen, Shalini R. Urs, Sung Hyon Myaeng
Publisher	Springer Verlag
Pages	111-122
Number of pages	12
ISBN (Electronic)	9783540206088
DOIs	https://doi.org/10.1007/978-3-540-24594-0_10
Publication status	Published - 2003
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2911
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-24594-0_10

Cite this

Fu, L., Goh, D. H. L., Foo, S. S. B., & Na, J. C. (2003). Collaborative querying through a hybrid query clustering approach. In T. M. Tengku Sembok, H. B. Zaman, H. Chen, S. R. Urs, & S. H. Myaeng (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 111-122). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2911). Springer Verlag. https://doi.org/10.1007/978-3-540-24594-0_10

Fu, Lin ; Goh, Dion Hoe Lian ; Foo, Schubert Shou Boon et al. / Collaborative querying through a hybrid query clustering approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Tengku Mohd Tengku Sembok ; Halimah Badioze Zaman ; Hsinchun Chen ; Shalini R. Urs ; Sung Hyon Myaeng. Springer Verlag, 2003. pp. 111-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Harnessing previously issued queries to facilitate collaborative querying is an approach that can help users in digital libraries and other information systems better meet their information needs. Here, the kernel step is to identify and cluster similar queries by mining the query logs. However because of the short lengths of queries, it is relatively difficult to cluster queries effectively using on the terms used since they cannot convey enough information. This paper introduces a hybrid method to cluster queries by utilizing both the query terms and the results returned to queries. Experiments show that this method outperforms existing query clustering techniques.",

author = "Lin Fu and Goh, {Dion Hoe Lian} and Foo, {Schubert Shou Boon} and Na, {Jin Cheon}",

year = "2003",

doi = "10.1007/978-3-540-24594-0_10",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "111--122",

editor = "{Tengku Sembok}, {Tengku Mohd} and Zaman, {Halimah Badioze} and Hsinchun Chen and Urs, {Shalini R.} and Myaeng, {Sung Hyon}",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

address = "Germany",

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Fu, L, Goh, DHL, Foo, SSB & Na, JC 2003, Collaborative querying through a hybrid query clustering approach. in TM Tengku Sembok, HB Zaman, H Chen, SR Urs & SH Myaeng (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2911, Springer Verlag, pp. 111-122. https://doi.org/10.1007/978-3-540-24594-0_10

Collaborative querying through a hybrid query clustering approach. / Fu, Lin; Goh, Dion Hoe Lian; Foo, Schubert Shou Boon et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Tengku Mohd Tengku Sembok; Halimah Badioze Zaman; Hsinchun Chen; Shalini R. Urs; Sung Hyon Myaeng. Springer Verlag, 2003. p. 111-122 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2911).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Collaborative querying through a hybrid query clustering approach

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AU - Goh, Dion Hoe Lian

AU - Foo, Schubert Shou Boon

AU - Na, Jin Cheon

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Y1 - 2003

N2 - Harnessing previously issued queries to facilitate collaborative querying is an approach that can help users in digital libraries and other information systems better meet their information needs. Here, the kernel step is to identify and cluster similar queries by mining the query logs. However because of the short lengths of queries, it is relatively difficult to cluster queries effectively using on the terms used since they cannot convey enough information. This paper introduces a hybrid method to cluster queries by utilizing both the query terms and the results returned to queries. Experiments show that this method outperforms existing query clustering techniques.

AB - Harnessing previously issued queries to facilitate collaborative querying is an approach that can help users in digital libraries and other information systems better meet their information needs. Here, the kernel step is to identify and cluster similar queries by mining the query logs. However because of the short lengths of queries, it is relatively difficult to cluster queries effectively using on the terms used since they cannot convey enough information. This paper introduces a hybrid method to cluster queries by utilizing both the query terms and the results returned to queries. Experiments show that this method outperforms existing query clustering techniques.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Tengku Sembok, Tengku Mohd

A2 - Zaman, Halimah Badioze

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Fu L, Goh DHL, Foo SSB, Na JC. Collaborative querying through a hybrid query clustering approach. In Tengku Sembok TM, Zaman HB, Chen H, Urs SR, Myaeng SH, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 111-122. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24594-0_10

Collaborative querying through a hybrid query clustering approach

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