Semantic relation extraction from socially-generated tags: A methodology for metadata generation

Miao Chen, Xiaozhong Liu, Jian Qin

Research output: Contribution to journalConference article

7 Scopus citations

Abstract

The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.

Original languageEnglish (US)
Pages (from-to)117-127
Number of pages11
JournalProceedings of the International Conference on Dublin Core and Metadata Applications
StatePublished - Dec 1 2008
Event8th Annual International Conference on Dublin Core and Metadata Applications, DC-2008 - Berlin, Germany
Duration: Sep 22 2008Sep 26 2008

Keywords

  • Metadata
  • Relation extraction
  • Search engine
  • Social semantics
  • Tags

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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