Title data
Ghawi, Raji ; Schönfeld, Mirco ; Pfeffer, Juergen:
Towards Semantic-based Social Network Analysis.
In: Sanniti di Baja, Gabriella ; Gallo, Luigi ; Yetongnon, Kokou ; Dipanda, Albert ; Castrillón-Santana, Modesto ; Chbeir, Richard
(ed.):
The 14th International Conference on Signal Image Technology & Internet Based Systems : Proceedings. -
Piscataway, NJ
: IEEE
,
2018
. - No. 18637972
ISBN 978-1-5386-9386-5
DOI: https://doi.org/10.1109/SITIS.2018.00091
Abstract in another language
We propose a semantic-based methodology for Social Network Analysis (SNA). This methodology addresses computations needed for SNA in a declarative way--in contrast to traditional SNA where computations are procedural. Our ingredients are semantic technologies: We define an ontology to represent graphs, their components (nodes, edges or paths), and the structural relationships between these components. We exploit reasoning capabilities of ontologies to infer structural relations between graph components. We also use ontological queries to perform computations needed in SNA. To demonstrate how does this approach work, we present three showcases of typical network analysis: basic metrics, triadic census, and betweenness centrality. The proposed approaches offer several computational opportunities for analyzing networks with respect to calculation of path-dependent centrality metrics, e.g. in distributed setups.
Further data
Item Type: | Article in a book |
---|---|
Refereed: | No |
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: | No |
DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science |
Date Deposited: | 18 Nov 2021 09:34 |
Last Modified: | 18 Nov 2021 09:34 |
URI: | https://eref.uni-bayreuth.de/id/eprint/67884 |