ANN: New SCOT Ontology Model
We are happy to announce that SCOT Ontology has been changed. Compared to the previous version, there are many changes in the model. In particular, the new property scot:taggingActivity can connect tags:Tagging class to describe and contain tagging activities in the Tag Cloud. Also we create the property scot:taggingAccount to describe online accounts for individual users. The following diagram illustrates the new model of SCOT ontology.
In addition, many property names has changed. For instance, the property scot:totalCooccurTags has changed as scot:totalCooccurringTags. Please check the SCOT ontology.
We are now writing the specification for SCOT ontology including a basic background, examples, and the changes. We will release this document as soon as possible.
papers about tag ontologies
We wrote two papers about tag ontologies. There are many efforts to bridge between social tagging and Semantic Web technologies. Although we focused on representation issues of tagging activities, it might be useful to know current efforts.
1. Hak-Lae Kim, Alexandre Passant, John Breslin, Simon Scerri, Stefan Decker, Review and Alignmnet of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging Spaces, In Proceedings of the 2nd International Conference on Semantic Computing, San Francisco, USA, 2008.
Abstract. As the number of Web 2.0 sites offering tagging facilities for the users’ voluntary content annotation increases, so do the efforts to analyze social phenomena resulting from generated tagging and folksonomies. Most of these efforts provide different views for the understanding of various web activities. Results from various experimental research should be utilized to improve existing approaches underlying tagging data and contribute further to weaving the Web. However, in practice, there are not enough solutions taking advantage of these results. Even though we can mine social relations via tagging data, it proves no worth for users if this data cannot be reused.
In this paper we propose a solution for tag data representation which allows data reuse across different tagging systems. To achieve this goal, we analyze current social tagging practices, existing folksonomy usage as well as Semantic Web approaches to data annotation and tagging. We survey and compare existing tag ontologies in an attempt to investigate mapping possibilities between different conceptual models. Finally, we present our method for federation among existing ontologies in order to generate re-usable, semantically-linked data that will underly tagging data.
2. Hak-Lae Kim, Simon Scerri, John Breslin, Stefan Decker, Hong-Gee Kim, The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies, In Proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, Germany, 2008.
Abstract. There is a growing interest on how we represent and share tagging data for the purpose of collaborative tagging systems. Conventional tags are not naturally suited for collaborative processes. Being free-text keywords, they are exposed to linguistic variations like case (upper vs lower), grammatical number (singular vs. plural) as well as human typing errors. Additionally, tags depend on the personal views of the world by individual users, and are not normalized for synonymy, morphology or any other mapping. The bottom line of the problem is that tags have no semantics whatsoever. Moreover, even if a user gives some semantics to a tag while using or viewing it, this meaning is not automatically shared with computers since it’s not defined in a machine-readable way. With tagging systems increasing in popularity each day, the evolution of this technology is hindered by this problem, since tagging metadata is not readily generated and shared. In this paper we discuss approaches to represent collaborative tagging activities at a semantic level, and present conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.