Semantic similarity measures for enhancing information retrieval in folksonomies

  • Authors:

  • Mohammed Nazim Uddin;Trong Hai Duong;Ngoc Thanh Nguyen;Xin-Min Qi;Geun Sik Jo

  • Affiliations:

  • School of Computer and Information Engineering, Inha University, South Korea;School of Computer and Information Engineering, Inha University, South Korea;Institute of Informatics, Wroclaw University of Technology, Poland;School of Computer and Information Engineering, Inha University, South Korea;School of Computer and Information Engineering, Inha University, South Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

Collaborative tagging systems, also known as folksonomies, enable a user to annotate various web resources with a free set of tags for sharing and searching purposes. Tags in a folksonomy reflect users' collaborative cognition about information. Tags play an important role in a folksonomy as a means of indexing information to facilitate search and navigation of resources. However, the semantics of the tags, and therefore the semantics of the resources, are neither known nor explicitly stated. It is therefore difficult for users to find related resources due to the absence of a consistent semantic meaning among tags. The shortage of relevant tags increases data sparseness and decreases the rate of information extraction with respect to user queries. Defining semantic relationships between tags, resources, and users is an important research issue for the retrieval of related information from folksonomies. In this research, a method for finding semantic relationships among tags is proposed. The present study considers not only the pairwise relationships between tags, resources, and users, but also the relationships among all three. Experimental results using real datasets from Flickr and Del.icio.us show that the method proposed here is more effective than previous methods such as LCH, JCN, and LIN in finding semantic relationships among tags in a folksonomy.