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Automatic query expansion based on tag recommendation

Authors:

Marcos GonçalvesAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 1985 - 1989

https://doi.org/10.1145/2396761.2398557

Published: 29 October 2012 Publication History

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Abstract

We here propose a new method for expanding entity related queries that automatically filters, weights and ranks candidate expasion terms extracted from Wikipedia articles related to the original query. Our method is based on state-of-the-art tag recommendation methods that exploit heuristic metrics to estimate the descriptive capacity of a given term. Originally proposed for the context of tags, we here apply these recommendation methods to weight and rank terms extracted from multiple fields of Wikipedia articles according to their relevance for the article. We evaluate our method comparing it against three state-of-the-art baselines in three collections. Our results indicate that our method outperforms all baselines in all collections, with relative gains in MAP of up to 14% against the best ones.

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Cited By

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M. Belém FM. Almeida JA. Gonçalves M(2020)Tagging and Tag RecommendationCyberspace10.5772/intechopen.82242Online publication date: 17-Jun-2020
https://doi.org/10.5772/intechopen.82242
Kumar RBhanodai GPamula R(2019)Book search using social information, user profiles and query expansion with Pseudo Relevance FeedbackApplied Intelligence10.1007/s10489-018-1383-z49:6(2178-2200)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10489-018-1383-z
Zhao YZhang YZhang BGao KLi P(2018)Recommending Queries by Extracting Thematic Experiences from Complex Search TasksEntropy10.3390/e2006045920:6(459)Online publication date: 13-Jun-2018
https://doi.org/10.3390/e20060459
Show More Cited By

Index Terms

Automatic query expansion based on tag recommendation
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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This paper highlights a number of problems which exist in the evaluation of existing image annotation and tag recommendation methods. Crucially, the collections used by these state-of-the-art methods contain a number of biases which may be exploited or ...

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Information & Contributors

Information

Published In

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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

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Citations

Cited By

View all

M. Belém FM. Almeida JA. Gonçalves M(2020)Tagging and Tag RecommendationCyberspace10.5772/intechopen.82242Online publication date: 17-Jun-2020
https://doi.org/10.5772/intechopen.82242
Kumar RBhanodai GPamula R(2019)Book search using social information, user profiles and query expansion with Pseudo Relevance FeedbackApplied Intelligence10.1007/s10489-018-1383-z49:6(2178-2200)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s10489-018-1383-z
Zhao YZhang YZhang BGao KLi P(2018)Recommending Queries by Extracting Thematic Experiences from Complex Search TasksEntropy10.3390/e2006045920:6(459)Online publication date: 13-Jun-2018
https://doi.org/10.3390/e20060459
Guo LSu XZhang LHuang GGao XDing Z(2018)Query Expansion Based on Semantic Related NetworkPRICAI 2018: Trends in Artificial Intelligence10.1007/978-3-319-97310-4_3(19-28)Online publication date: 27-Jul-2018
https://doi.org/10.1007/978-3-319-97310-4_3
Deng YMaghraoui KGriffin TAgarwal VTamilselvam SSharnagat RAlexander TGómez NCramer CBivens AJadav DValli-Hasham ZWahlmeier K(2017)Advanced search system for IT support servicesIBM Journal of Research and Development10.1147/JRD.2016.262865861:1(3:27-3:40)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1147/JRD.2016.2628658
Belém FAlmeida JGonçalves M(2017)A survey on tag recommendation methodsJournal of the Association for Information Science and Technology10.1002/asi.2373668:4(830-844)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1002/asi.23736
Zhu RDeng YSarkar SEl Maghraoui KRamasamy HBivens A(2016)Towards More Effective Solution Retrieval in IT Support Services Using Systems LogService-Oriented Computing10.1007/978-3-319-46295-0_52(730-744)Online publication date: 20-Sep-2016
https://doi.org/10.1007/978-3-319-46295-0_52
Xu BLin HLin Y(2016)Assessment of learning to rank methods for query expansionJournal of the Association for Information Science and Technology10.1002/asi.2347667:6(1345-1357)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1002/asi.23476
Bo Zhang Bin Zhang Shubo Zhang Daming Sun (2015)Selecting expansion terms based on path-constrained term-relationship graphs2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)10.1109/FSKD.2015.7382159(1460-1464)Online publication date: Aug-2015
https://doi.org/10.1109/FSKD.2015.7382159
Nguyen QDo SNguyen TAberer K(2015)Tag-Based Paper Retrieval: Minimizing User Effort with Diversity AwarenessDatabase Systems for Advanced Applications10.1007/978-3-319-18120-2_30(510-528)Online publication date: 9-Apr-2015
https://doi.org/10.1007/978-3-319-18120-2_30
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