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Network Analysis of Semantic Connections in Heterogeneous Social Spaces

Network Analysis of Semantic Connections in Heterogeneous Social Spaces. Sheila Kinsella, Andreas Harth, John G. Breslin. Overview. Object-Centred Sociality Social Network Models Semantic Web Datasets Results Conclusions. Object-Centred Sociality.

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Network Analysis of Semantic Connections in Heterogeneous Social Spaces

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  1. Network Analysis of Semantic Connections in Heterogeneous Social Spaces Sheila Kinsella, Andreas Harth, John G. Breslin

  2. Overview • Object-Centred Sociality • Social Network Models • Semantic Web • Datasets • Results • Conclusions

  3. Object-Centred Sociality • People don’t just connect, they connect through shared objects • Real life: jobs, events, activities • Online: blogs, images, web links • Objects are an important part of online sociality

  4. Social Network Models Affiliation network One-mode network Semantic network

  5. Semantic Web • Sir Tim Berners-Lee et al., Scientific American, 2001: • “An extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” • Enables expression of data in a way that computers can understand • Interoperabilityand increased connectivity is possible through common formats

  6. Resource Description Language (RDF) • Graph model used to express Semantic Web data • Each node is an instance of a class • Each link is an instance of a property (e.g. relationship) • Classes and properties defined in schemas • Namespaces indicate the schema to which classes and properties belong

  7. Friend-of-a-Friend (FOAF) • RDF schema for describing people and the relationships that exist between them • Integrated with many other vocabularies on the Semantic Web • Can be used to express explicit and implicit links

  8. Datasets • We start with FOAF file of Tim Berners-Lee • Extract[1] all documents within 5 links of root node • Idenfity two subgraphs of interest: • People-only network • Composed only of people directly connected to the root node, or indirectly via other people • Object-centred network • Contains all nodes who are directly or indirectly connected to the root node • Repeat for FOAF file of Andreas Harth [1] Using Semantic Web Search Engine (http://swse.org)

  9. Effect of object inclusion: Tim Berners-Lee network Objects 361 People 691 Links 1446 People 349 Links 450 People-only network Object-centred network

  10. Effect of object inclusion: Andreas Harth network Objects 7842 Links 47286 People 9011 People 16671 Links 24875 Object-centred network People-only network

  11. Most common namespaces in the datasets

  12. Object and link types: Tim Berners-Lee network Most common classes Most common relationships

  13. Object and link types: Tim Berners-Lee network Most common classes Most common relationships

  14. Object and link types: Tim Berners-Lee network Most common classes Most common relationships

  15. Object and link types: Tim Berners-Lee network Most common classes Most common relationships

  16. Object and link types: Andreas Harth network Most common classes Most common relationships

  17. Object and link types: Andreas Harth network Most common classes Most common relationships

  18. Object and link types: Andreas Harth network Most common classes Most common relationships

  19. Conclusions • We investigate the online social networks formed by direct interpersonal links and indirect links via objects • Dataset is from a range of semantically-enabled sources • Including objects in the network gives us additional information and reveals hidden links • Future work: • Look at which types of objects are responsible for hidden links and evaluate their relevance • Analyse object-centred network to locate sets of objects relevant to a given person

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