at Google ScholarTitle 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 Faculties |
| 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 |