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Extracting Ego-Centric Social Networks from Linked Open Data

Title data

Ghawi, Raji ; Schönfeld, Mirco ; Pfeffer, Juergen:
Extracting Ego-Centric Social Networks from Linked Open Data.
In: Barnaghi, Payam ; Gottlob, Georg ; Manolopoulos, Yannis ; Tzouramanis, Theodoros ; Vakali, Athena (ed.): 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2019) : Proceedings. - New York : Association for Computing Machinery , 2019 . - pp. 471-477
ISBN 978-1-72815-037-6
DOI: https://doi.org/10.1145/3350546.3352570

Abstract in another language

Linked Open Data (LOD) refers to freely available data on the WWW that are typically represented using Resource Description Framework (RDF). LOD is an invaluable source of rich and structured information, and enables a wide range of new applications, such as Social Network Analysis (SNA). In this paper, we address the extraction of social networks from LOD using SPARQL language, and we present various patterns to extract ego-centric networks. We also present two case studies: i) influence networks of intellectuals, and ii) co-acting networks, to demonstrate the applicability and usefulness of the approach

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: Faculties > Faculty of Languages and Literature
Faculties > Faculty of Languages and Literature > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung
Faculties > Faculty of Languages and Literature > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung > Juniorprofessur Datenmodellierung und interdisziplinäre Wissensgenerierung - Juniorprof. Dr. Mirco Schönfeld
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 18 Nov 2021 09:03
Last Modified: 18 Nov 2021 09:03
URI: https://eref.uni-bayreuth.de/id/eprint/67881