BibTeX
@MISC{Ouksili_themeidentification,
author = {Hanane Ouksili},
title = {Theme Identification in RDF Graphs?},
year = {}
}
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Abstract
Abstract. An increasing number of RDF datasets is published on the Web. A user willing to use these datasets will first have to explore them in order to determine which information is relevant for his specific needs. To facilitate this exploration, we present an approach allowing to provide a thematic view of a given RDF dataset, making it easier to target the rel-evant resources and properties. Our approach combines a density-based graph clustering algorithm with semantic criteria in order to identify clusters, each one corresponding to a theme. Prior to clustering, the ini-tial RDF graph is simplified, and user preferences are mapped into a set of transformations applied to the graph. Once the clusters are identified, labels are extracted to express their semantics. In this paper, we describe the main features of our approach to generate a set of themes from an RDF dataset.
