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.
In this website you can find information about folksonomies, their strenghts and weaknesses, and Sem4Tags an approach to improve multilingual folksonomies using semantic web techniques. In addition, there is information about the developed demo software to associate tags with semantic entities.
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.
The SCOT(Social Semantic Cloud Of Tags) ontology is to semantically represent the structure and semantics of a collection of tags and to represent social networks among users based on the tags.
A. Plangprasopchok, K. Lerman, and L. Getoor. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 949--958. New York, NY, USA, ACM, (2010)
P. Mika. The Semantic Web - ISWC 2005, Proceedings of the 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10, volume 3729 of Lecture Notes in Computer Science, page 522-536. Springer, (2005)
F. Limpens, F. Gandon, and M. Buffa. Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on, (September 2008)
N. Tomuro, and A. Shepitsen. Proceedings of the 2009 Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic Resources, page 42--50. Stroudsburg, PA, USA, Association for Computational Linguistics, (2009)