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Latest revision as of 19:17, 11 November 2009
Read but not relevant
- Au Yeung, C. M., Gibbins, N. and Shadbolt, N. (2007). Understanding the Semantics of Ambiguous Tags in Folksonomies. In: The International Workshop on Emergent Semantics and Ontology Evolution (ESOE2007) at ISWC/ASWC 2007, 12 November 2007, Busan, South Korea.
Article saved as: yeung_2007 Bipartitioning, too technical
- Angeletou, S., Sabou, M., Specia, L. Motta, E. (2007). Bridging the gap between folksonomies and the semantic web: an experience report
Article saved as: angeletou_2007 Combines it with existing online ontologies
- Grahl, M., Hotho, A., Stumme, G. (2007). Conceptual clustering of social bookmarking sites.
Article saved as: grahl_2007 Too technical, bipartitioning
- Peters, I. (2006). Against folksonomies: indexing blogs and podcasts for corporate knowledge management. Online information 2006, Conference Proceedings, London: Learned Information Europe, Ltd.
Article saved as: peters_2006 This article was not relevant, but in the lit review it was giving useful resources. I looked those resources and they were talking in general about folksonomies and tags and their problems, but they were not giving any solutions.
- Tonkin, E. (2006). Folksonomies: the fall and rise of plain-text tagging. Ariande, 47.
Not relevant, it talks about rich documentation-how to save urls, titles and create tags in combination with Dublin Core
- Kolbitsch, J. (2007). WordFlickr: a solution to the vocabulary problem in social tagging systems.
Not relevant, general overview about what tagging is and which are the most popular software.
- Dextre, Clarke S. (2001). Thesaural relationships, In: Relationships in the organization of knowledge, Carol A. Bean (edr.) Boston: Kluwer
Book Not relevant, if focuses on thesauri relationships, which they cannot be applied in ontologies, as thesauri fail to create strict relationships like ontologies do. There are three types of thesauri relationships: (a) equivalence, (b) hierarchical and (c) associative.
- This article is user-driven. The research conducted explores how users tag and what is that they are trying to describe when they tag.