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Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL

Bavarian School of Public Policy, Technical University of Munich, 80333 Munich, Germany
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This paper is an extended version of our paper published in the proceedings of the 24th International Conferences on Conceptual Structures, Marburg, Germany, 1–4 July 2019.
Information 2020, 11(7), 361; https://doi.org/10.3390/info11070361
Received: 7 June 2020 / Revised: 23 June 2020 / Accepted: 2 July 2020 / Published: 12 July 2020
(This article belongs to the Special Issue Conceptual Structures 2019)
Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks among entities. This enables the application of de-facto techniques from Social Network Analysis (SNA) to study social relations and interactions among entities, providing deep insights into their latent social structure. View Full-Text
Keywords: linked open data; social networks; RDF; SPARQL algebra; extraction patterns linked open data; social networks; RDF; SPARQL algebra; extraction patterns
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MDPI and ACS Style

Ghawi, R.; Pfeffer, J. Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL. Information 2020, 11, 361. https://doi.org/10.3390/info11070361

AMA Style

Ghawi R, Pfeffer J. Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL. Information. 2020; 11(7):361. https://doi.org/10.3390/info11070361

Chicago/Turabian Style

Ghawi, Raji; Pfeffer, Jürgen. 2020. "Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL" Information 11, no. 7: 361. https://doi.org/10.3390/info11070361

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