Approximate string-matching with q-grams and maximal matches
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
The art of computer programming, volume 1 (3rd ed.): fundamental algorithms
Geometry and Meaning
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Integrating Folksonomies with the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies
WSKS '08 Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society
The fundamentals of iSPARQL: a virtual triple approach for similarity-based semantic web tasks
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Detecting similarities in ontologies with the SOQA-SimPack toolkit
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
A more specific events classification to improve crawling techniques
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems
Proceedings of the 1st International Workshop on Linked Web Data Management
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Nowadays the popularity of tag clouds in websites is increased notably, but its generation is criticized because its lack of control causes it to be more likely to produce inconsistent and redundant results. It is well known that if tags are freely chosen (instead of taken from a given set of terms), synonyms (multiple tags for the same meaning), normalization of words and even, heterogeneity of users are likely to arise, lowering the efficiency of content indexing and searching contents. To solve this problem, we have designed the Maximum Similarity Measure (MaSiMe) a dynamic and flexible similarity measure that is able to take into account and optimize several considerations of the user who wishes to obtain a free-of-redundancies tag cloud. Moreover, we include an algorithm to effectively compute the measure and a parametric study to determine the best configuration for this algorithm.