Group items tagged

KIM is a software platform for: * Semantic annotation of text At more length: automatic ontology population and open-domain dynamic semantic annotation of unstructured and semi-structured content for Semantic Web and KM applications * Indexing and retrieval (semantically-enabled and IE-enhanced search technology) * Query and exploration of formal knowledge * Co-occurrence tracking and ranking of entities * Entity popularity timelines analysis

Andreas Hotho's talk more specifically addressed the search for methods to identify tags which describe the same concept (or a more specific / a more general concept respectively) within a folksonomy. He suggested two approaches: 1. Applying measures directly to folksonomy statistics, allowing to describe tags as a vector; e.g. co-occurrence frequency and FolkRank could serve as a similarity measure (with these two having a tendency towards high-frequency tags) or a cosine method (which is more likely to produce "siblings") 2. Looking up tags in an external thesaurus/vocabulary (for instance achieving semantic grounding by mapping a tag and its most similar tags with Wordnet Synsets)

"The debate within the Web community over the optimal means by which to organize information often pits formalized classications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for “popular” sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags."

The paper shows that the tags users choose are not chaotic, but rather quickly converge to a common descriptive set of tags that is almost unchanging over time. Perhaps once the tags have stabilized, coherent URI-based identification schemes could emerge?

Nice paper, thanks. Categories / tags / subjects / topics / issues ... that's what I'm working with right now. p.s. sure would be nice if the email notification included the source URL. I'm far more likely to download the PDF when I see something like www2007.org/paper635.pdf