Instanced-based mapping between thesauri and folksonomies
|Instanced-based mapping between thesauri and folksonomies|
|Author(s)||Wartena C., Brussee R.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Conformal mapping, Information theory, Metadata, Semantics, Bookmarking services, Collaborative annotations, Folksonomies, Human judgments, Semantic interoperability, Similarity measures, Special properties, Topic structures, Web-based systems, Wikipedia, Semantic Web)|
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Instanced-based mapping between thesauri and folksonomies is a 2008 conference paper written in English by Wartena C., Brussee R. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
The emergence of web based systems in which users can annotate items, raises the question of the semantic interoperability between vocabularies originating from collaborative annotation processes, often called folksonomies, and keywords assigned in a more traditional way. If collections are annotated according to two systems, e.g. with tags and keywords, the annotated data can be used for instance based mapping between the vocabularies. The basis for this kind of matching is an appropriate similarity measure between concepts, based on their distribution as annotations. In this paper we propose a new similarity measure that can take advantage of some special properties of user generated metadata. We have evaluated this measure with a set of articles from Wikipedia which are both classified according to the topic structure of Wikipedia and annotated by users of the bookmarking service del.icio.us. The results using the new measure are significantly better than those obtained using standard similarity measures proposed for this task in the literature, i.e., it correlates better with human judgments. We argue that the measure also has benefits for instance based mapping of more traditionally developed vocabularies.
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