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. The main contribution of this work is to combine existing clustering techniques with some semantic preferences set by the user to identify the themes. The proposed approach comprises three steps: (i) capturing users preferences, (ii) applying a clustering algorithm to identify the themes and (iii) extracting labels describing each of them. In this paper, we describe the main features of our approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bader, G.D., Hogue, C.W.V.: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4 (2003)
Castano, S., Ferrara, A., Montanelli, S.: Thematic clustering and exploration of linked data. In: SeCO Book, pp. 157–175. Springer, Heidelberg (2012)
Castano, S., Ferrara, A., Montanelli, S.: Mining topic clouds from social data. In: Proc. MEDES, pp. 108–112 (2013)
Etminani, K., Naghibzadeh, M.: Overlapped ontology partitioning based on semantic similarity measures. In: Fifth International Symposium on Telecommunicatioin (2010)
Fortunato, S.: Community detection in graphs. CoRR, abs/0906.0612 (2009)
Gargi, U., Lu, W., Mirrokni, V., Yoon, S.: Large-Scale Community Detection on YouTube for Topic Discovery and Exploration. In: ICWSM, pp. 486–489 (2011)
Gergely, P., Imre, D., Illés, F., Tamás, V.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Hu, W., Qu, Y., Cheng, G.: Matching large ontologies: A divide-and-conquer approach. Data & Knowledge Engineering 67, 140–160 (2008)
Ouksili, H., Kedad, Z., Lopes, S.: A tool for theme identification in RDF graphs. In: Métais, E., Roche, M., Teisseire, M. (eds.) NLDB 2014. LNCS, vol. 8455, pp. 262–265. Springer, Heidelberg (2014)
Pires, C., Sousa, P., Kedad, Z., Salgado, A.: Summarizing ontology-based schemas in pdms. In: ICDEW, pp. 239–244 (2010)
Shahsavand Baghdadi, H., Ranaivo-Malançon, B.: An Automatic Topic Identification Algorithm. Journal of Computer Science 7(9), 1363–1367 (2011)
Voigt, M., Tietz, V., Piccolotto, N., Meißner, K.: Attract me!: How could end-users identify interesting resources? In: Proc. WIMS, vol. 36, pp. 1–12 (2013)
Zhang, X., Cheng, G., Qu, Y.: Ontology summarization based on rdf sentence graph. In: Proc. WWW, pp. 707–716. ACM (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ouksili, H., Kedad, Z., Lopes, S. (2014). Theme Identification in RDF Graphs. In: Ait Ameur, Y., Bellatreche, L., Papadopoulos, G.A. (eds) Model and Data Engineering. MEDI 2014. Lecture Notes in Computer Science, vol 8748. Springer, Cham. https://doi.org/10.1007/978-3-319-11587-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-11587-0_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11586-3
Online ISBN: 978-3-319-11587-0
eBook Packages: Computer ScienceComputer Science (R0)