research-article

Query biased snippet generation in XML search

Authors:

Yi ChenAuthors Info & Claims

SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data

Pages 315 - 326

https://doi.org/10.1145/1376616.1376651

Published: 09 June 2008 Publication History

Abstract

Snippets are used by almost every text search engine to complement ranking scheme in order to effectively handle user searches, which are inherently ambiguous and whose relevance semantics are difficult to assess. Despite the fact that XML is a standard representation format of web data, research on generating result snippets for XML search remains untouched.

In this paper we present a system, eXtract, which addresses this important yet open problem. We identify that a good XML result snippet should be a self-contained meaningful information unit of a small size that effectively summarizes this query result and differentiates it from others, according to which users can quickly assess the relevance of the query result. We have designed and implemented a novel algorithm to satisfy these requirements and verified its efficiency and effectiveness through experiments.

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Cited By

Chen QChen JZhou XCheng GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Enhancing Dataset Search with Compact Data SnippetsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657837(1093-1103)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657837
Gao SNg Y(2022)Generating extractive sentiment summaries for natural language user queries on productsACM SIGAPP Applied Computing Review10.1145/3558053.355805422:2(5-20)Online publication date: 17-Aug-2022
https://dl.acm.org/doi/10.1145/3558053.3558054
Lima RSantos RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)On Extractive Summarization for Profile-centric Neural Expert Search in AcademiaProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531713(2331-2335)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531713
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Index Terms

Query biased snippet generation in XML search
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data

June 2008

1396 pages

ISBN:9781605581026

DOI:10.1145/1376616

General Chairs:
Laks V. S. Lakshmanan
University of British Columbia, Canada
,
Raymond T. Ng
University of British Columbia, Canada
,
Dennis Shasha
New York University, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 June 2008

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SIGMOD/PODS '08

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SIGMOD/PODS '08: SIGMOD/PODS '08 - International Conference on Management of Data

June 9 - 12, 2008

Vancouver, Canada

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Chen QChen JZhou XCheng GHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Enhancing Dataset Search with Compact Data SnippetsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657837(1093-1103)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657837
Gao SNg Y(2022)Generating extractive sentiment summaries for natural language user queries on productsACM SIGAPP Applied Computing Review10.1145/3558053.355805422:2(5-20)Online publication date: 17-Aug-2022
https://dl.acm.org/doi/10.1145/3558053.3558054
Lima RSantos RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)On Extractive Summarization for Profile-centric Neural Expert Search in AcademiaProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531713(2331-2335)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531713
Zhang ZYu JWang GYuan YChen L(2021)Key-core: cohesive keyword subgraph exploration in large graphsWorld Wide Web10.1007/s11280-021-00926-yOnline publication date: 6-Aug-2021
https://doi.org/10.1007/s11280-021-00926-y
Gatterbauer W(2020)Databases will visualize queries tooProceedings of the VLDB Endowment10.14778/3402755.34028054:12(1498-1501)Online publication date: 3-Jun-2020
https://dl.acm.org/doi/10.14778/3402755.3402805
Jiang SLiamruk P(2020)Effects of SERP Information on Academic Search Behaviours2020 - 5th International Conference on Information Technology (InCIT)10.1109/InCIT50588.2020.9310951(33-38)Online publication date: 21-Oct-2020
https://doi.org/10.1109/InCIT50588.2020.9310951
Bessai-Mechmache FHammouche KAlimazighi Z(2020)A Genetic Algorithm-Based XML Information Retrieval Model2020 21st International Arab Conference on Information Technology (ACIT)10.1109/ACIT50332.2020.9300048(1-5)Online publication date: 28-Nov-2020
https://doi.org/10.1109/ACIT50332.2020.9300048
Chen WHagen MStein BPotthast MCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)A User Study on Snippet GenerationThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210149(1033-1036)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210149
Mohanty MRamanath MRanu SGanguly NRamakrishnan RSarawagi SRoy S(2018)KlustreeProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3152494.3152509(265-272)Online publication date: 11-Jan-2018
https://dl.acm.org/doi/10.1145/3152494.3152509
Akilesh BKumar NReddy BSingh M(2018)TRAFAN: Road traffic analysis using social media web pages2018 10th International Conference on Communication Systems & Networks (COMSNETS)10.1109/COMSNETS.2018.8328290(655-659)Online publication date: Jan-2018
https://doi.org/10.1109/COMSNETS.2018.8328290
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