Preliminary results in tag disambiguation using DBpedia

The availability of tag-based user-generated content for a variety of Web resources (music, photos, videos, text, etc.) has largely increased in the last years. Users can assign tags freely and then use them to share and retrieve information. However, tag-based sharing and retrieval is not optimal due to the fact that tags are plain text labels without an explicit or formal meaning, and hence polysemy and synonymy should be dealt with appropriately. To ameliorate these problems, we propose a context-based tag disambiguation algorithm that selects the meaning of a tag among a set of candidate DBpedia entries, using a common information retrieval similarity measure. The most similar DBpedia en-try is selected as the one representing the meaning of the tag. We describe and analyze some preliminary results, and discuss about current challenges in this area

Upper Tag Ontology (UTO) For Integrating Social Tagging Data

Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube)

Why Do Folksonomies Need Semantic Web Technologies?

This paper is to investigate some general features of social tagging and folksonomies along with their advantages and disadvantages, and to present an overview of a tag ontology that can be used to represent tagging data at a semantic level using Semantic Web technologies. Several tag ontologies have been developed with a specific purpose and used in various websites. However, in order to represent tagging data at semantic level existing tag ontologies need to be interlinked, since individual tag ontology cannot represent overall features of tagging activities. After introducing conceptual overview of tagging and folksonomies and tag ontologies, we will propose the combinational model for linking tag ontologies

Collaborative semantic structuring of folksonomies (short article)

The advent of tagging and folksonomies for organizing shared resources on the social Web brought promising opportunities to help communities of users capture their knowledge. However, the lack of semantics, or the spelling variations between tags lowers the potentials for browsing and exploring these data. To overcome these limitations, we propose exploiting the interactions between the users and the systems to validate or correct semantic analysis automatically applied to the tags. This process is based upon our model of the assistance of folksonomies enrichment which supports conflictual points of view. Several strategies can then be applied to propose novel browsing facilities to users

Linking Folksonomies and Ontologies for Supporting Knowledge Sharing: a State of the Art

Deliverable of ISICIL ANR-funded projectSocial tagging systems have recently become very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations: tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This report compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web

Rapprocher les ontologies et les folksonomies pour la gestion des connaissances partagées : un état de l'art.

International audienceCes dernières années, le “tagging social” s'est imposé au sein du Web 2.0 comme le principal moyen de classification de données en très grand nombre. Néanmoins, l'emploi pour la classification d'ensembles de tags non contrôlés, appelés folksonomies, pose plusieurs problèmes : le problème de l'ambiguïté (un tag dénotant plusieurs concepts), le problème de variations d'écritures (plusieurs tags dénotant un même concept) et le manque d'assistance à l'exploitation de ces structures (notamment pour la recherche ou l'échange d'informations) en l'absence de représentations explicites de ces connaissances partagées. Cet article présente un état de l'art comparatif et une discussion sur les travaux proposant une évolution de ces systèmes à base de folksonomies, en tenant compte, notamment, des travaux existant dans le domaine de la représentation des connaissances, en particulier ceux à base d'ontologies

A State of the Art on Social Network Analysis and its Applications on a Semantic Web

The increasingly popular web 2.0 sites provide the largest social network ever analyzed - users are now considered as plain web resources. Some researchers apply classical methods of social network analysis to such networks; others provide models to leverage the semantics of their representation. We present a state of the art of these two approaches and propose an architecture to merge and exploit the best features of each

Sémantique des folksonomies: structuration collaborative et assistée

National audienceThe advent of tagging and folksonomies for organizing shared ressources on the social Web brought promising opportunities to help communities of users capture their knowledge. However, the lack of semantics, or the spelling variations between tags lowers the potentials for browsing and exploring these data. To overcome these limitations, we propose exploiting the interactions between the users and the systems to validate or correct semantic analysis automatically applied to the tags. This process is based upon our model of the assistance of folksonomies enrichment which supports conflictual points of view. Several strategies can then be applied to propose novel browsing facilities to users

Semantic Social Network Analysis

International audienceSocial Network Analysis (SNA) tries to understand and exploit the key features of social networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge social network. Classical methods from social network analysis (SNA) have been applied to such online networks. In this paper, we propose leveraging semantic web technologies to merge and exploit the best features of each domain. We present how to facilitate and enhance the analysis of online social networks, exploiting the power of semantic social network analysis