Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.

@article{jaeschke2008discovering, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd}, title = {Discovering Shared Conceptualizations in Folksonomies}, editor = {Finin, T. and Mizoguchi, R. and Staab, S.}, booktitle = {Semantic Web and Web 2.0}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, publisher = {Elsevier}, address = {New York}, year = {2008}, volume = {6}, number = {1}, pages = {38--53}, url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b}, doi = {10.1016/j.websem.2007.11.004}, issn = {1570-8268}, keywords = {2008, analysis, concept, folksonomy, formal, l3s, ol_web2.0, tagging, trias, wp5, methods_concepts, emergentsemantics_evidence}, abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.} }

%0 = article %A = Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd %B = Semantic Web and Web 2.0 %C = New York %D = 2008 %I = Elsevier %T = Discovering Shared Conceptualizations in Folksonomies %U = http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b

Ontologies are widely used and play important roles in applications related to knowledge management, artificial intelligence, natural language processing, etc. Measuring the semantic similarity between ontological concepts is necessary in applications that use ontologies. This paper presents a survey of approaches to compute ontological concept similarity. A taxonomy showing the classification of approaches is introduced. The advantages and disadvantages of each approach are discussed.

@inproceedings{le2007current, author = {Le, Duy Ngan and Goh, A.E.S.}, title = {Current Practices in Measuring Ontological Concept Similarity}, journal = {Semantics, Knowledge and Grid, Third International Conference on}, year = {2007}, pages = {266-269}, doi = {10.1109/SKG.2007.16}, keywords = {concept, similarity, toread, ontology}, abstract = {Ontologies are widely used and play important roles in applications related to knowledge management, artificial intelligence, natural language processing, etc. Measuring the semantic similarity between ontological concepts is necessary in applications that use ontologies. This paper presents a survey of approaches to compute ontological concept similarity. A taxonomy showing the classification of approaches is introduced. The advantages and disadvantages of each approach are discussed.} }

%0 = inproceedings %A = Le, Duy Ngan and Goh, A.E.S. %D = 2007 %T = Current Practices in Measuring Ontological Concept Similarity