research-article

iSearch: an interpretation based framework for keyword search in relational databases

Authors:

Mong Li LeeAuthors Info & Claims

KEYS '12: Proceedings of the Third International Workshop on Keyword Search on Structured Data

Pages 3 - 10

https://doi.org/10.1145/2254736.2254741

Published: 20 May 2012 Publication History

Abstract

Keyword search has become an effective information retrieval method for structured data. Existing works in relational database keyword search have addressed the problems of finding and evaluating candidate results. However, given that keyword queries are inherently ambiguous, it is often the case that candidate results do not match users' search intention. In this paper, we analyze the limitations of current keyword search techniques and introduce the problem of generating and ranking keyword query interpretations. We propose a novel 3-phase keyword search paradigm which consists of: (1) the ability to predict query interpretations; (2) incorporate user feedback to to remove keyword ambiguities; (3) a ranking model to evaluate a query interpretation.

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Cited By

M.S. VHaritsa J(2019)Operator implementation of Result Set Dependent KWS scoring functionsInformation Systems10.1016/j.is.2019.101465(101465)Online publication date: Nov-2019
https://doi.org/10.1016/j.is.2019.101465
Xu YYang Y(2018)COKES: Continuous Top-k Keyword Search in Relational DatabasesGeo-Spatial Knowledge and Intelligence10.1007/978-981-13-0896-3_21(205-217)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-981-13-0896-3_21
Wang YWang NZhou L(2017)Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational DatabasesScientific Programming10.1155/2017/76130262017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/7613026
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Published In

cover image ACM Conferences

KEYS '12: Proceedings of the Third International Workshop on Keyword Search on Structured Data

May 2012

78 pages

ISBN:9781450311984

DOI:10.1145/2254736

General Chairs:
Ling Tok Wang
National University of Singapore, Singapore
,
Ge Yu
Northeastern University, China
,
Jiaheng Lu
Renmin University of China, China
,
Wei Wang
The University of New South Wales, Australia

Copyright © 2012 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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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2012

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Ministry of Education - Singapore

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SIGMOD/PODS '12

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SIGMOD

SIGMOD/PODS '12: International Conference on Management of Data

May 20, 2012

Arizona, Scottsdale

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Citations

Cited By

M.S. VHaritsa J(2019)Operator implementation of Result Set Dependent KWS scoring functionsInformation Systems10.1016/j.is.2019.101465(101465)Online publication date: Nov-2019
https://doi.org/10.1016/j.is.2019.101465
Xu YYang Y(2018)COKES: Continuous Top-k Keyword Search in Relational DatabasesGeo-Spatial Knowledge and Intelligence10.1007/978-981-13-0896-3_21(205-217)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-981-13-0896-3_21
Wang YWang NZhou L(2017)Keyword Query Expansion Paradigm Based on Recommendation and Interpretation in Relational DatabasesScientific Programming10.1155/2017/76130262017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/7613026
Shan YLi MChen Y(2017)Constructing target-aware results for keyword search on knowledge graphsData & Knowledge Engineering10.1016/j.datak.2017.02.001110(1-23)Online publication date: Jul-2017
https://doi.org/10.1016/j.datak.2017.02.001
Xu Y(2017)Scalable top-k keyword search in relational databasesCluster Computing10.1007/s10586-017-1232-6Online publication date: 6-Oct-2017
https://doi.org/10.1007/s10586-017-1232-6
Zhou JLiu YYu Z(2015)Improving the Effectiveness of Keyword Search in Databases Using Query LogsWeb-Age Information Management10.1007/978-3-319-21042-1_16(193-206)Online publication date: 6-Jun-2015
https://doi.org/10.1007/978-3-319-21042-1_16
Wang CDing JHu B(2012)Ranking Algorithms for Keyword Search over Relational DatabasesAdvanced Materials Research10.4028/www.scientific.net/AMR.605-607.2291605-607(2291-2296)Online publication date: Dec-2012
https://doi.org/10.4028/www.scientific.net/AMR.605-607.2291
Thein MThwin M(2012)Answering Top-k Keyword Queries on Relational DatabasesInternational Journal of Information Retrieval Research10.4018/ijirr.20120701032:3(36-57)Online publication date: 1-Jul-2012
https://dl.acm.org/doi/10.4018/ijirr.2012070103

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