ACoAR: a method for the automatic classification of annotated resources.
ABSTRACT We propose a classification method that automatically classifies annotated resources under the concepts of a classification system represented by an ontology. We use two well known systems used to classify web pages, del.icio.us for the folksonomy information and DMOZ for an existing ontology, to validate the method. Results obtained provide a correct classification rate of resources of 78%, rising to 93% when using an adequate threshold.
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ABSTRACT: This paper presents conceptual assumptions about the interaction between the structural specificities of a thesaurus and the quality of a thesaurus-based application output. So far hardly any literature exists that discusses thesaurus modelling requirements with respect to the following thesaurus-specific application areas: classifying, indexing, autocom-plete, query expansion, recommendation and glossaries. By looking at these application areas the authors compare the structural attributes of SKOS and discuss their functional relevance. The authors conclude that taking these assumptions into account can significantly support application-oriented thesaurus modelling hence incrementally improving thesaurus-based applications in terms of modelling scope and effort. An empirical testing of these assumptions is subject to future work.Proceedings the 7th International Conference on Semantic Systems, I-SEMANTICS 2011, Graz, Austria, September 7-9, 2011; 01/2011
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ABSTRACT: This paper presents an automatic method to group resources of collaborative-social tagging systems in semantic categories. The main goal is to self-adapt the system to represent the current knowledge.Proceedings of the 6th International Conference on Knowledge Capture (K-CAP 2011), June 26-29, 2011, Banff, Alberta, Canada; 01/2011