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Author, Editor

Author(s):

Theobald, Martin
Schenkel, Ralf
Weikum, Gerhard

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Editor(s):

Baeza-Yates, Ricardo A.
Ziviani, Nivio
Marchionini, Gary
Moffat, Alistair
Tait, John

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Not MPII Editor(s):

Baeza-Yates, Ricardo A.
Ziviani, Nivio
Marchionini, Gary
Moffat, Alistair
Tait, John

BibTeX cite key*:

TheobaldSW05

Title, Booktitle

Title*:

Efficient and Self-Tuning Incremental Query Expansion for Top-k Query Processing


TheobaldSTW.pdf (520.43 KB)

Booktitle*:

28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2005)

Event, URLs

URL of the conference:

www.sigir2005.org

URL for downloading the paper:

http://doi.acm.org/10.1145/1076077

Event Address*:

Salvador, Brazil

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

15 August 2005

Event End Date:

19 August 2005

Publisher

Name*:

ACM

URL:


Address*:

New York, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

242-249

Year*:

2005

VG Wort Pages:

42

ISBN/ISSN:

1-59593-034-5

Sequence Number:


DOI:




Note, Abstract, ©

Note:

Acceptance ratio 1:5

(LaTeX) Abstract:

We present a novel approach for efficient and self-tuning query expansion that is embedded into a top-k query processor with candidate pruning. Traditional query expansion methods select expansion terms whose thematic similarity to the original query terms is above some specified threshold, thus generating a disjunctive query with much higher dimensionality. This poses three major problems: 1) the need for hand-tuning the expansion threshold, 2) the potential topic dilution with overly aggressive expansion, and 3) the drastically increased execution cost of a high-dimensional query. The method developed in this paper addresses all three problems by dynamically and incrementally merging the inverted lists for the potential expansion terms with the lists for the original query terms. A priority queue is used for maintaining result candidates, the pruning of candidates is based on Fagin's family of top-k algorithms, and optionally probabilistic estimators of candidate scores can be used for additional pruning. Experiments on the TREC collections for the 2004 Robust and Terabyte tracks demonstrate the increased efficiency, effectiveness, and scalability of our approach.

Keywords:

Incremental merge - Probabilistic candidate pruning - Query expansion - Top-k ranking



Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Databases and Information Systems Group

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{TheobaldSW05,
AUTHOR = {Theobald, Martin and Schenkel, Ralf and Weikum, Gerhard},
EDITOR = {Baeza-Yates, Ricardo A. and Ziviani, Nivio and Marchionini, Gary and Moffat, Alistair and Tait, John},
TITLE = {Efficient and Self-Tuning Incremental Query Expansion for Top-k Query Processing},
BOOKTITLE = {28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2005)},
PUBLISHER = {ACM},
YEAR = {2005},
PAGES = {242--249},
ADDRESS = {Salvador, Brazil},
ISBN = {1-59593-034-5},
NOTE = {Acceptance ratio 1:5},
}


Entry last modified by Christine Kiesel, 04/14/2006
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Editor(s)
Ralf Schenkel
Created
04/11/2005 10:20:31 AM
Revisions
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Editor(s)
Christine Kiesel
Christine Kiesel
Adriana Davidescu
Adriana Davidescu
Adriana Davidescu
Edit Dates
14.04.2006 12:59:27
20.01.2006 14:25:53
06.01.2006 10:12:16
06.01.2006 10:09:18
06.01.2006 10:07:59
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