Using tagFlake for condensing navigable tag hierarchies from tag clouds

Luigi Di Caro, Kasim Candan, Maria Luisa Sapino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

We present the tagFlake system, which supports semantically informed navigation within a tag cloud. tagFlake relies on TMine for organizing tags extracted from textual content in hierarchical organizations, suitable for navigation, visualization, classification, and tracking. TMine extracts the most significant tag/terms from text documents and maps them onto a hierarchy in such a way that descendant terms are contextually dependent on their ancestors within the given corpus of documents. This provides tagFlake with a mechanism for enabling navigation within the tag space and for classification of the text documents based on the contextual structure captured by the created hierarchy. tagFlake is language neutral, since it does not rely on any natural language processing technique and is unsupervised.

Original languageEnglish (US)
Title of host publicationKDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
Pages1069-1072
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008 - Las Vegas, NV, United States
Duration: Aug 24 2008Aug 27 2008

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
CountryUnited States
CityLas Vegas, NV
Period8/24/088/27/08

    Fingerprint

Keywords

  • Algorithms
  • Human factors

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Di Caro, L., Candan, K., & Sapino, M. L. (2008). Using tagFlake for condensing navigable tag hierarchies from tag clouds. In KDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining (pp. 1069-1072). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1401890.1402021