Ontologies are enabling technology for the Semantic Web. They are a means for people to state what they mean by formal terms used in data that they might generate or consume. Folksonomies are an emergent phenomenon of the social web. They are created a
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
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.
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
"by letting users tag (...), we're (building) systems that, like the Web itself, do a better job of letting individuals create value for one another, often without realizing it."
"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"
"TagOntology is about identifying and formalizing a conceptualization of the activity of tagging, and building technology that commits to the ontology at the semantic level."
Clarity regarding controlled vocabularies, taxonomies, thesauri, ontologies, and metamodels. With all the scuttlebut going around about folksonomies and tagging, these are important terms to understand. In the process of tagging, it's pretty noticeable
Part of the allure of classifying things by assigning tags to them is that the user can give free reign to sloppiness. There is no authority —human or computational— passing judgment on the appropriateness or validity of tags, because tags have to mak
Part of the allure of classifying things by assigning tags to them is that the user can give free reign to sloppiness. There is no authority —human or computational— passing judgment on the appropriateness or validity of tags, because tags have to mak
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.
Gruber said he saw four different ways of adding meta-tags to material, ranging from the loosest to the most strict:
1.Folksonomy (informal, user-defined)
2.Controlled vocabularies (the user must deploy a set of defined terms)
3.Taxonomy (Pre-defined terms in which specific terms are subsets of more general terms)
4.Ontology (A rich set of relationships is mapped out among all the terms)
K. Siorpaes and M. Hepp, myOntology: The Marriage of Collective Intelligence and Ontology Engineering, in Proceedings of the Workshop Bridging the Gap between Semantic Web and Web 2.0 at the ESWC 2007. LNCS Springer: Innsbruck, Austria, June 7, 2007.
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.
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.
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.
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.
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)
F. Limpens, F. Gandon, and M. Buffa. Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on, (September 2008)
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)
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)