1. 1 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic Social Network Analysis Kyunglag Kwon Intelligent Information System Lab., Korea University Jan. 21st, 2013 in Big Lab Seminar - G Erétéo, F Gandon, O Corby, M Buffa - arXiv preprint arXiv:0904.3701, 2009 - arxiv.org

2. 2 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Outline Introduction SNA and Semantic Web Technologies Semantic SNA Architecture Paths in RDF graph with CORESE (Semantic Search Engine) Measure Centrality with SPARQL  Parameterized centrality SemSNA: an ontology of SNA Results Conclusions and Perspectives

3. 3 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Introduction Social Network Analysis (SNA) Understand and exploit the key features of social networks Manage life cycle of social networks and predict their evolution A new framework for applying SNA to RDF representation of social data (FOAF ontology) Computation of different measures of centrality in RDF-based social network representations Degree, Closeness, Betweenness SemSNA Ontology that describes the SNA features such as centrality

4. 4 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Related Works SNA and Semantic Web Technologies RDF and SPARQL, RDFS and OWL to represent and exchange knowledge online FOAF, RELATIONSHIP and Semantically Interlinked Online Community (SIOC) ontology In some research: lose too much knowledge (i.e., foaf:knows, foaf:interest) Social Construction of Technology (SCOT) Bridges folksonomies and ontologies to leverage the semantics of tags Peter Mika: Links users, tags and resources with ternary edges In all these experiments, Lack of expressivity of the social network representations Therefore, Direct exploitation of the RDF representations to utilize the rich information

5. 5 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture Architecture of Semantic SNA Abstract social network constructs from domain ontologies on schemas by having them extend suggested primitives Enrich social data with new annotations such as the SNA indices

6. 6 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture

7. 7 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture

8. 8 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture

9. 9 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture

10. 10 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture

11. 11 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture Schema of SemSNA: an ontology of SNA Strategic position based on Freeman’s definition of centrality

12. 12 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture New Version of SemSNA

13. 13 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture Example of enriching the social data with new annotations such as the SNA indices

14. 14 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Semantic SNA Architecture Example of a parameterized degree centrality Only family relations are considered ( = ‘family’)

15. 15 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Performance Study Dataset 2020 FOAF profiles from flickr.com 326,617 foaf:knows, 126,308 foaf:person For identification of the most important betweenness centralities Stopped after 50,000 graph pattern matching Shows power law distribution of betweenness centralities Betweenness centrality # of actors

16. 16 Intelligent Information System Lab., Department of Computer and Information Science, Korea University Conclusions In this paper, Leverage the semantics of social data in a machine readable format Novel algorithm based on adaptation of classical SNA definitions Future works Development of iterative algorithms and methods to manage evolutions of ever-changing networks