research-article

A semantic clustering-based approach for searching and browsing tag spaces

Authors:

Jan-Willem van Dam,

Frederik Hogenboom,

Flavius FrasincarAuthors Info & Claims

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

Pages 1693 - 1699

https://doi.org/10.1145/1982185.1982538

Published: 21 March 2011 Publication History

Abstract

Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, in this paper, we propose the Semantic Tag Clustering Search, a framework able to cope with these needs. The framework consists of three parts: removing syntactic variations, creating semantic clusters, and utilizing the obtained clusters to improve search and exploration of tag spaces. For removing syntactic variations, we use the normalized Levenshtein distance, and the cosine similarity measure based on tag co-occurrences. For creating semantic clusters, we improve an existing non-hierarchical clustering technique. Using our framework, we are able to find more clusters and achieve a higher precision than the original method. The advantages of a cluster-based approach for searching and browsing through tag spaces have been exploited in Xplore-Flickr.com, the implementation of our framework.

References

[1]

F. Abel. GroupMe!, 2010. From: http://groupme.org/.

[2]

F. Abel, N. Henze, and D. Krause. Analyzing Ranking Algorithms in Folksonomy Systems. Technical Report, L3S Research Center, 2008. From: http://groupme.org/papers/techreport-ranking-in-folksonomies.pdf.

[3]

F. Abel, N. Henze, and D. Krause. Ranking in Folksonomy Systems: Can Context Help? In J. G. Shanahan, S. Amer-Yahia, I. Manolescu, Y. Zhang, D. A. Evans, A. Kolcz, K.-S. Choi, and A. Chowdhury, editors, 17th ACM Conference on Information and Knowledge Management (CIKM 2008), pages 1429--1430. ACM, 2008.

Digital Library

[4]

S. Bao, G. Xue, X. Wu, Y. Yu, B. Fei, and Z. Su. Optimizing Web Search Using Social Annotations. In C. L. Williamson, M. E. Zurko, P. F. Patel-Schneider, and P. J. Shenoy, editors, 16th World Wide Web Conference (WWW 2007), pages 501--510. ACM, 2007.

Digital Library

[5]

G. Begelman, P. Keller, and F. Smadja. Automated Tag Clustering: Improving Search and Exploration in the Tag Space. In L. A. Carr, D. C. D. Roure, A. Iyengar, C. A. Goble, and M. Dahlin, editors, 15th World Wide Web Conference (WWW 2006), pages 22--26. ACM, 2006.

[6]

S. Chapman. SimMetrics Package, 2010. From: http://sourceforge.net/projects/simmetrics/.

[7]

F. Echarte, J. J. Astrain, A. Córdoba, and J. Villadangos. Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies. In M. D. Lytras, J. M. Carroll, E. Damiani, and R. D. Tennyson, editors, 1st World Summit on The Knowledge Society (WSKS 2008), volume 5288 of LNCS, pages 557--564. Springer, 2008.

Digital Library

[8]

R. W. Hamming. Error Detecting and Error Correcting Codes. Bell System Technical Journal, 26(2): 147--160, 1950.

[9]

A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. FolkRank: A Ranking Algorithm for Folksonomies. In Workshop on Information Retrieval (FGIR 2006), 2006. From: http://www.uni-hildesheim.de/de/8168.htm#id22532.

[10]

R. Johnson. Spring - Platform for Java Entrerprise Applications, 2010. From: http://www.springsource.org/.

[11]

V. I. Levenshtein. Binary Codes Capable of Correction Deletions, Insertions, and Reversals. Soviet Physics Doklady, 10(8): 707--710, 1966.

[12]

M. E. J. Newman and M. Girvan. Finding and Evaluating Community Structure in Networks. Physical Review E, 69(2): 1--15, 2004.

[13]

A. Pothen, H. D. Simon, and K.-P. Liou. Partitioning Sparse Matrices with Eigenvectors of Graphs. SIAM Journal on Matrix Analysis and Applications, 11(3): 430--452, 1990.

Digital Library

[14]

P. Schmitz. Inducing Ontology from Flickr Tags. In L. A. Carr, D. C. D. Roure, A. Iyengar, C. A. Goble, and M. Dahlin, editors, 15th World Wide Web Conference (WWW 2006), pages 206--209. ACM, 2006.

[15]

L. Specia and E. Motta. Integrating Folksonomies with the Semantic Web. In E. Franconi, M. Kifer, and W. May, editors, 4th European Semantic Web Conference (ESWC 2007), volume 4519 of LNCS, pages 503--517. Springer, 2007.

Digital Library

[16]

J. W. van Dam, D. Vandic, F. Hogenboom, and F. Frasincar. Searching and Browsing Tag Spaces Using the Semantic Tag Clustering Search Framework. In I. Harris and M.-L. Shyu, editors, 4th IEEE International Conference on Semantic Computing (ICSC 2010), pages 436--439. IEEE Computer Society, 2010.

Digital Library

[17]

J. W. van Dam, D. Vandic, F. Hogenboom, and F. Frasincar. XploreFlickr.com, 2010. From: http://www.xploreflickr.com/.

Cited By

Zomahoun D(2019)A Semantic Collaborative Clustering Approach Based on Confusion Matrix2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2019.00112(688-692)Online publication date: Nov-2019
https://doi.org/10.1109/SITIS.2019.00112
Radelaar JBoor AVandic DVan Dam JFasincar F(2018)Improving search and exploration in tag spaces using automated tag clusteringJournal of Web Engineering10.5555/2685119.268512613:3-4(277-301)Online publication date: 21-Dec-2018
https://dl.acm.org/doi/10.5555/2685119.2685126
Hai-Jew S(2015)Sampling Public Sentiment Using Related Tags (and User-Created Content) Networks from Social Media PlatformsEnhancing Qualitative and Mixed Methods Research with Technology10.4018/978-1-4666-6493-7.ch014(331-390)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-6493-7.ch014
Show More Cited By

Index Terms

A semantic clustering-based approach for searching and browsing tag spaces
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Recommendations

A semantic-based approach for searching and browsing tag spaces

In this paper we propose the Semantic Tag Clustering Search (STCS) framework for enhancing the user experience in interacting with tagging systems. This framework consists of three parts. The first part deals with syntactic variations by finding ...
Searching and Browsing Tag Spaces Using the Semantic Tag Clustering Search Framework
ICSC '10: Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing

Many of the existing cloud tagging systems are unable to cope with the syntactic and semantic tag variations during user search and browse activities. As a solution to this problem, we propose the Semantic Tag Clustering Search, a framework which is ...
Improving the exploration of tag spaces using automated tag clustering
ICWE'11: Proceedings of the 11th international conference on Web engineering

Due to the increasing popularity of tagging, it is important to overcome challenges resulting from the free nature of tagging, such as the use of synonyms, homonyms, syntactic variations, etc. The Semantic Tag Clustering Search (STCS) framework deals ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing

March 2011

1868 pages

ISBN:9781450301138

DOI:10.1145/1982185

Conference Chairs:
William Chu
Tunghai University, TaiChung, Taiwan
,
W. Eric Wong
University of Texas at Dallas, Richardson, Texas
,
Program Chairs:
Mathew J. Palakal
Indiana University Purdue University, Indianapolis
,
Chih-Cheng Hung
Southern Polytechnic State University, Marietta

Copyright © 2011 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]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 March 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC'11

Sponsor:

SIGAPP

SAC'11: The 2011 ACM Symposium on Applied Computing

March 21 - 24, 2011

TaiChung, Taiwan

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
218
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zomahoun D(2019)A Semantic Collaborative Clustering Approach Based on Confusion Matrix2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2019.00112(688-692)Online publication date: Nov-2019
https://doi.org/10.1109/SITIS.2019.00112
Radelaar JBoor AVandic DVan Dam JFasincar F(2018)Improving search and exploration in tag spaces using automated tag clusteringJournal of Web Engineering10.5555/2685119.268512613:3-4(277-301)Online publication date: 21-Dec-2018
https://dl.acm.org/doi/10.5555/2685119.2685126
Hai-Jew S(2015)Sampling Public Sentiment Using Related Tags (and User-Created Content) Networks from Social Media PlatformsEnhancing Qualitative and Mixed Methods Research with Technology10.4018/978-1-4666-6493-7.ch014(331-390)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-6493-7.ch014
Xu GZong YJin PPan RWu Z(2015)KIPTCJournal of Intelligent Information Systems10.1007/s10844-013-0262-745:1(95-112)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1007/s10844-013-0262-7
Koh JChiu I(2014)An Efficient Approach for Mining Top-k High Utility Specialized Query Expansions on Social Tagging SystemsDatabase Systems for Advanced Applications10.1007/978-3-319-05813-9_24(361-376)Online publication date: 2014
https://doi.org/10.1007/978-3-319-05813-9_24
García-Plaza AZubiaga AFresno VMartínez R(2013)Tag Cloud ReorganizationSocial Media Mining and Social Network Analysis10.4018/978-1-4666-2806-9.ch009(140-155)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-2806-9.ch009
Bracamonte TSchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Multimedia information retrieval on the social webProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2487941(349-354)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2487788.2487941
Koh JChiang KChiu I(2013)The Strategies for Supporting Query Specialization and Query Generalization in Social Tagging SystemsProceedings of the 18th International Conference on Database Systems for Advanced Applications - Volume 782710.1007/978-3-642-40270-8_14(164-178)Online publication date: 22-Apr-2013
https://dl.acm.org/doi/10.1007/978-3-642-40270-8_14
Koh JShongwe NCho C(2012)A multi-level hierarchical index structure for supporting efficient similarity search on tag sets2012 Sixth International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2012.6240436(1-12)Online publication date: May-2012
https://doi.org/10.1109/RCIS.2012.6240436
Pérez García-Plaza AZubiaga AFresno VMartínez R(2012)Reorganizing cloudsExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.02.10839:10(9483-9493)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1016/j.eswa.2012.02.108
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents