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Extracting ego-centric social networks from linked open data

  • Technical University of Munich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
EditorsPayam Barnaghi, Georg Gottlob, Yannis Manolopoulos, Theodoros Tzouramanis, Athena Vakali
PublisherAssociation for Computing Machinery, Inc
Pages471-477
Number of pages7
ISBN (Electronic)9781450369343
DOIs
StatePublished - 14 Oct 2019
Event19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019 - Thessaloniki, Greece
Duration: 13 Oct 201917 Oct 2019

Publication series

NameProceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019

Conference

Conference19th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019
Country/TerritoryGreece
CityThessaloniki
Period13/10/1917/10/19

Keywords

  • Ego-centric networks
  • Extraction patterns
  • Linked open data
  • SPARQL
  • Social network analysis

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