when you mix different metadata you loss on quality. Yes you can merge them but then you need to square the number of dictionaries respect than languages. AArrgh
Interesting examples, terrible argument! No substantiated claims whatsoever! Shirky obviously thinks he is pretty smart and does not have to produce any real arguments based on evidence.
"They are built to be human-usable (...) are targeted primarily for storage/retrieval of personal information and serendipitous discovery of group information . (...) The development communities for each are abuzz with ideas for exploiting the structure"
This piece is based on two talks I gave in the spring of 2005 -- one at the O'Reilly ETech conference in March, entitled "Ontology Is Overrated", and one at the IMCExpo in April entitled "Folksonomies & Tags: The rise of user-developed classification." Th
This specification describes the FOAF language, defined as a dictionary of named properties and classes using W3C's RDF technology.
FOAF is a project devoted to linking people and information using the Web. Regardless of whether information is in people's heads, in physical or digital documents, or in the form of factual data, it can be linked. FOAF integrates three kinds of network: social networks of human collaboration, friendship and association; representational networks that describe a simplified view of a cartoon universe in factual terms, and information networks that use Web-based linking to share independently published descriptions of this inter-connected world. FOAF does not compete with socially-oriented Web sites; rather it provides an approach in which different sites can tell different parts of the larger story, and by which users can retain some control over their information in a non-proprietary format.
The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows the full materialization of the Semantic Web, as these systems are cheap, extendable, scalable and respond quickly to user needs. However, for the sake of knowledge workflow, one needs to find a compromise between the ungoverned nature of folksonomies and the controlled vocabulary of domain-experts. In this paper, we address this concern by first devising a method that automatically combines folksonomies with domain-expert ontologies resulting in an enriched folksonomy. We then introduce a new algorithm based on frequent itemsets mining that efficiently learns an ontology over the concepts present in the enriched folksonomy. Moreover, we propose a new benchmark for ontology evaluation, which is used in the context of information finding, since this is one of the leading motivations for using ontologies in social tagging systems, to quantitatively assess our method. We conduct experiments on real data and empirically show the effectiveness of our approach.
C. Damme, M. Hepp, и K. Siorpaes. In Proceedings of the ESWC Workshop ``Bridging the Gap between Semantic
Web and Web 2.0'' (SemNet 2007), стр. 57--70. (2007)