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 own needs. To facilitate this exploration, we present a system which provides a thematic view of a given RDF dataset, making it easier to target the relevant resources and properties. Our system combines a density-based graph clustering algorithm with semantic clustering criteria in order to identify clusters, each one corresponding to a theme. In this paper, we will give an overview of our approach for theme identification and we will present our system along with a scenario illustrating its main features.
This work was supported by Electricity of France (EDF R&D).
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Ouksili, H., Kedad, Z., Lopes, S. (2014). A Tool for Theme Identification in RDF Graphs. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_39
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DOI: https://doi.org/10.1007/978-3-319-07983-7_39
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07982-0
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