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Social Network Analysis, Semantic Web and Learning Networks
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Social Network Analysis, Semantic Web and Learning Networks

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Session 2 of the Learning Networks Social Networks Seminar. It presents a recap of SNA terms, and introduces the Semantic Web and how it could be applied to Learning Networks.

Session 2 of the Learning Networks Social Networks Seminar. It presents a recap of SNA terms, and introduces the Semantic Web and how it could be applied to Learning Networks.

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  • Thank you for your attention, and I hope to see you at the Career day

Transcript

  • 1. LN SNA Seminar session 2
    • Adriana Berlanga & Rory Sie
  • 2. Schedule!
    • Session 3 ( March 13th , 13-15h, Chiba 1.42/video conference):
      • Miguel-Angel Sicilia and Karina Cela (University of Alcalá)
    • Session 4 (April 17th, 13-15h, Chiba 1.42):
      • Yiwei Cao (RWTH Aachen)
    • Session 5 (June 19th, 14-16h , Chiba 1.42/video conference):
      • Julita Vassileva (video conference)
  • 3. SNA & Semantic Web (and LN)
    • Rory Sie
  • 4. Outline
    • Recap
    • Semantic Web
    • Use for LN
  • 5. Recap
  • 6. Network measures
    • network: density, connectivity, centralization
    • community: factions, cliques
    • individual: betweenness, degree, closeness
  • 7. Network measures
    • network: density, connectivity, centralization
    • community: factions, cliques
    • individual: betweenness, degree, closeness
  • 8. Network measures
    • network: density, connectivity, centralization
    • community: factions, cliques
    • individual: betweenness, degree, closeness
  • 9. Data storage
    • Adjacency matrix (R, UCINET)
    • GML/XGMML (Cytoscape, Gephi)
    • Pajek Network (Pajek, UCINET)
  • 10. Data storage
    • Adjacency matrix (R, UCINET)
    • GML/XGMML (Cytoscape, Gephi)
    • Pajek Network (Pajek, UCINET)
    • <?xml version=&quot;1.0&quot; encoding=&quot;UTF-8&quot; standalone=&quot;yes&quot;?><graph label=&quot;PLN for ID &quot; directed=&quot;1&quot;>
    • <node id=&quot; n26 &quot; label=&quot;n26&quot;><att type=&quot;string&quot; name=&quot;PeerName&quot; value=&quot; Rory Sie &quot;/></node><node id=&quot; n27 &quot; label=&quot;n27&quot;><att type=&quot;string&quot; name=&quot;PeerName&quot; value=&quot; Adriana Berlanga &quot;/></node><edge id=&quot;e0&quot; label=&quot;e0&quot; source=&quot; n26 &quot; target=&quot; n27 &quot;><att type=&quot;string&quot; name=&quot;interaction&quot; value=&quot;colleague&quot;&quot;/>
      • </edge>
        • </graph>
  • 11. Data storage
    • Adjacency matrix (R, UCINET)
    • GML/XGMML (Cytoscape, Gephi)
    • Pajek Network (Pajek, UCINET)
  • 12. Analysis But what if you want to do this real-time / online?
  • 13. CytoscapeWeb
    • http://cytoscapeweb.cytoscape.org
    • Cytoscape, but online
    • Great for visualization
  • 14. Connect R to web
    • RemoteREngine package
  • 15.  
  • 16. Web 1.0
  • 17. Web 2.0
  • 18. Semantic Web (3.0) writes writes about place writes about resource
  • 19. Semantic Web (3.0) writes writes about place writes about resource
  • 20. Semantic Web (3.0) writes writes about place writes about resource learns from friend of mother of follows
  • 21. learning networks
  • 22. Knowledge Representation
    • RDF
    • Triple store (e.g. Sesame)
    • Query language (e.g. SPARQL)
  • 23. Knowledge Representation
    • RDF
    • Triple store (e.g. Sesame)
    • Query language (e.g. SPARQL)
    “ Rory” “ learns from” “ Adriana”
  • 24. Knowledge Representation
    • RDF
    • Triple store (e.g. Sesame)
    • Query language (e.g. SPARQL)
    “ Rory” “ learns from” “ Adriana” subject predicate object
  • 25. Knowledge Representation
    • RDF
    • Triple store (e.g. Sesame)
    • Query language (e.g. SPARQL)
    subject predicate object triple “ Rory” “ learns from” “ Adriana”
  • 26. Example data
    • < http://ln.org/person/ Rory > < http://ln.org/ learns_from > < http://ln.org/person/ Adriana >
  • 27. Knowledge Representation
    • RDF
    • Triple store (e.g. Sesame)
    • Query language (e.g. SPARQL)
  • 28. http://www.ag-nbi.de/research/swrlengine/
  • 29. Knowledge Representation
    • RDF
    • Triple store (e.g. Sesame)
    • Query language (e.g. SPARQL)
  • 30. Example data
    • < http://ln.org/person/ Rory > < http://ln.org/ learns_from > < http://ln.org/person/ Adriana >
  • 31. SPARQL
    • SELECT ?tutor
    • WHERE
    • {
    • < http://ln.org/person/Rory > < http://ln.org/learns_from > ?tutor
    • }
  • 32. Result
    • < http://ln.org/person/Adriana >
  • 33. How can this help us?
    • store learning networks data in RDF
    • use SNA to analyse network, individuals, communities, topics
  • 34. CSCL script and roles Capuano et al , 2011)
  • 35. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree = 5
  • 36. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<family> = 2
  • 37. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<friend> = 1
  • 38. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<peer learner> = 2
  • 39. SPARQL n-degree select ?y count(?x) as ?degree where{ {?x $path ?y filter(match($path, star( param[type] ))) filter(pathLength($path) <= param[length] ) } UNION { ?y $path ?x filter(match($path, star( param[type] ))) filter(pathLength($path) <= param[length] ) } } group by ?y
  • 40. Summary
    • Semantic Web and Social Network Analysis help us make sense of different types of data that are in a social network
  • 41. Questions?
    • [email_address]
    • http://www.open.ou.nl/rse
    • openrory, maisonpoublon
    • Rory Sie
    • openrse
    • http://nl.linkedin.com/in/rorysie
    • thebigbangrory.blogspot.com
  • 42. References
    • R project ( http://www.r-project.org/ )
    • UCINET ( https://sites.google.com/site/ucinetsoftware/home )
    • Gephi ( http://gephi.org/ )
    • Cytoscape ( http://www.cytoscape.org )
    • Capuano, N., Laria, G., Mazzoni, E., Pierri, A., & Mangione, G. R. (2011). Improving Role Taking in CSCL Script Using SNA and Semantic Web. 2011 IEEE 11th International Conference on Advanced Learning Technologies , 636-637. Ieee. doi:10.1109/ICALT.2011.197
    • Berners-lee, B. T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American .
    • Guillaume Ereteo’s PhD defense ( http://www.slideshare.net/ereteog/phd-defense-semantic-social-network-analysis )
    • Microformats (http://microformats.org/)