Article

Concept-based interactive query expansion

Authors:

Bruno M. Fonseca,

Berthier Ribeiro-Neto,

Nivio ZivianiAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 696 - 703

https://doi.org/10.1145/1099554.1099726

Published: 31 October 2005 Publication History

Abstract

Despite the recent advances in search quality, the fast increase in the size of the Web collection has introduced new challenges for Web ranking algorithms. In fact, there are still many situations in which the users are presented with imprecise or very poor results. One of the key difficulties is the fact that users usually submit very short and ambiguous queries, and they do not fully specify their information needs. That is, it is necessary to improve the query formation process if better answers are to be provided. In this work we propose a novel concept-based query expansion technique, which allows disambiguating queries submitted to search engines. The concepts are extracted by analyzing and locating cycles in a special type of query relations graph. This is a directed graph built from query relations mined using association rules. The concepts related to the current query are then shown to the user who selects the one concept that he interprets is most related to his query. This concept is used to expand the original query and the expanded query is processed instead. Using a Web test collection, we show that our approach leads to gains in average precision figures of roughly 32%. Further, if the user also provides information on the type of relation between his query and the selected concept, the gains in average precision go up to roughly 52%.

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Cited By

Chandra GDwivedi S(2024)Query Expansion Using Proposed Location-Based Algorithm for Hindi–English CLIR: Analyzing Three Test CollectionsInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142459001838:05Online publication date: 11-May-2024
https://doi.org/10.1142/S0218001424590018
Vishwakarma DKumar S(2023)Analysis of Recent Query Expansion Techniques for Information Retrieval SystemsProceedings of the International Conference on Intelligent Computing, Communication and Information Security10.1007/978-981-99-1373-2_29(375-383)Online publication date: 4-Jul-2023
https://doi.org/10.1007/978-981-99-1373-2_29
Singh SSuleman H(2022)Investigating User Control to Mitigate Bias When Searching African Historical DataFrom Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries10.1007/978-3-031-21756-2_37(456-463)Online publication date: 7-Dec-2022
https://doi.org/10.1007/978-3-031-21756-2_37
Show More Cited By

Index Terms

Concept-based interactive query expansion
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Information retrieval query processing
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

Copyright © 2005 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: 31 October 2005

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CIKM05

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CIKM05: Conference on Information and Knowledge Management

October 31 - November 5, 2005

Bremen, Germany

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Chandra GDwivedi S(2024)Query Expansion Using Proposed Location-Based Algorithm for Hindi–English CLIR: Analyzing Three Test CollectionsInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142459001838:05Online publication date: 11-May-2024
https://doi.org/10.1142/S0218001424590018
Vishwakarma DKumar S(2023)Analysis of Recent Query Expansion Techniques for Information Retrieval SystemsProceedings of the International Conference on Intelligent Computing, Communication and Information Security10.1007/978-981-99-1373-2_29(375-383)Online publication date: 4-Jul-2023
https://doi.org/10.1007/978-981-99-1373-2_29
Singh SSuleman H(2022)Investigating User Control to Mitigate Bias When Searching African Historical DataFrom Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries10.1007/978-3-031-21756-2_37(456-463)Online publication date: 7-Dec-2022
https://doi.org/10.1007/978-3-031-21756-2_37
Vuong TAndolina SJacucci GRuotsalo T(2021)Does More Context Help? Effects of Context Window and Application Source on Retrieval PerformanceACM Transactions on Information Systems10.1145/347405540:2(1-40)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3474055
Petridis SShin HChilton L(2021)SymbolFinder: Brainstorming Diverse Symbols Using Local Semantic NetworksThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474757(385-399)Online publication date: 10-Oct-2021
https://dl.acm.org/doi/10.1145/3472749.3474757
Sheetrit EFyodorov YRaiber FKurland OHasibi FFang YAizawa A(2021)Recommending Search Queries in Documents Using Inter N-Gram SimilaritiesProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3471158.3472252(211-220)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3471158.3472252
Qi KWang RLu QWang XJing NNiu DChen HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Dual Learning for Query Generation and Query Selection in Query Feeds RecommendationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481910(4065-4074)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481910
Han FNiu DChen HGuo WYan SLong BGupta RLiu YShah MRajan STang JPrakash B(2020)Meta-Learning for Query Conceptualization at Web ScaleProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403357(3064-3073)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403357
Kazi MGuo WGao HLong Bd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Incorporating User Feedback into Sequence to Sequence Model TrainingProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412714(2557-2564)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412714
Liao ZSong YZhou D(2020)Query SuggestionQuery Understanding for Search Engines10.1007/978-3-030-58334-7_8(171-203)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_8
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