Tag-based navigation and visualization

Dimitar Dimitrov, Denis Helic, Markus Strohmaier

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

Allowing users to organize content by tagging resources in webbased systems has led to the emergence of the so-called SocialWeb. Tags turned out to be helpful not only for giving recommendations and improving search in social tagging systems but also for enhancing information access by navigating. In this chapter, we will cover much of the pioneer research work that has studied tag-based navigation and visualization. After giving a short overview of the social tagging process and its specifics, we provide an extensive description of the typical user interfaces and visualization techniques characteristic for social tagging systems. As the efficiency of tag-based navigation depends on structuring tagging data, we also provide a review of the state of the art algorithms for tag clustering. Before we conclude, we demonstrate how tag-based navigation can be modeled and discuss the intrinsic navigability of social tagging systems from various theoretic perspectives.

LanguageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag Heidelberg
Pages181-212
Number of pages32
DOIs
StatusPublished - 1 Jan 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10100 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Tagging
Navigation
Visualization
User interfaces
Web-based
User Interface
Recommendations
Clustering
Cover
Resources
Demonstrate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Dimitrov, D., Helic, D., & Strohmaier, M. (2018). Tag-based navigation and visualization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 181-212). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10100 LNCS). Springer Verlag Heidelberg. https://doi.org/10.1007/978-3-319-90092-6_6

Tag-based navigation and visualization. / Dimitrov, Dimitar; Helic, Denis; Strohmaier, Markus.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag Heidelberg, 2018. p. 181-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10100 LNCS).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Dimitrov, D, Helic, D & Strohmaier, M 2018, Tag-based navigation and visualization. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10100 LNCS, Springer Verlag Heidelberg, pp. 181-212. https://doi.org/10.1007/978-3-319-90092-6_6
Dimitrov D, Helic D, Strohmaier M. Tag-based navigation and visualization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag Heidelberg. 2018. p. 181-212. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-90092-6_6
Dimitrov, Dimitar ; Helic, Denis ; Strohmaier, Markus. / Tag-based navigation and visualization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag Heidelberg, 2018. pp. 181-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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