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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 relevant 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 initial 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.

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Theme Identification in RDF Graphs

Ouksili

Kedad

Lopes

2014

Model and Data Engineering

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PatEx: Pattern Oriented RDF Graphs Exploration

Ouksili¹,

Kedad²,

Lopes³

et al. 2016

Natural Language Processing and Information Systems

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YourDataStories: Transparency and Corruption Fighting Through Data Interlinking and Visual Exploration

2018

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Attract me!

Cited by 3 publications

References 33 publications

Theme Identification in RDF Graphs

Theme Identification in RDF Graphs

PatEx: Pattern Oriented RDF Graphs Exploration

YourDataStories: Transparency and Corruption Fighting Through Data Interlinking and Visual Exploration

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