LN SNA Seminar session 2 <ul><li>Adriana Berlanga & Rory Sie </li></ul>
Schedule! <ul><li>Session 3 ( March 13th , 13-15h, Chiba 1.42/video conference): </li></ul><ul><ul><li>Miguel-Angel Sicili...
SNA & Semantic Web (and LN) <ul><li>Rory Sie </li></ul>
Outline <ul><li>Recap </li></ul><ul><li>Semantic Web </li></ul><ul><li>Use for LN </li></ul>
Recap
Network measures <ul><li>network: density, connectivity, centralization </li></ul><ul><li>community: factions, cliques </l...
Network measures <ul><li>network: density, connectivity, centralization </li></ul><ul><li>community: factions, cliques </l...
Network measures <ul><li>network: density, connectivity, centralization </li></ul><ul><li>community: factions, cliques </l...
Data storage <ul><li>Adjacency matrix (R, UCINET) </li></ul><ul><li>GML/XGMML (Cytoscape, Gephi) </li></ul><ul><li>Pajek N...
Data storage <ul><li>Adjacency matrix (R, UCINET) </li></ul><ul><li>GML/XGMML (Cytoscape, Gephi) </li></ul><ul><li>Pajek N...
Data storage <ul><li>Adjacency matrix (R, UCINET) </li></ul><ul><li>GML/XGMML (Cytoscape, Gephi) </li></ul><ul><li>Pajek N...
Analysis But what if you want to do this real-time / online?
CytoscapeWeb <ul><li>http://cytoscapeweb.cytoscape.org </li></ul><ul><li>Cytoscape, but online </li></ul><ul><li>Great for...
Connect R to web <ul><li>RemoteREngine package </li></ul>
 
Web 1.0
Web 2.0
Semantic Web (3.0)  writes writes about place writes about resource
Semantic Web (3.0)  writes writes about place writes about resource
Semantic Web (3.0)  writes writes about place writes about resource learns from friend of mother of follows
learning networks
Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. S...
Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. S...
Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. S...
Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. S...
Example data <ul><li>< http://ln.org/person/ Rory > < http://ln.org/ learns_from > < http://ln.org/person/ Adriana > </li>...
Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. S...
http://www.ag-nbi.de/research/swrlengine/
Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. S...
Example data <ul><li>< http://ln.org/person/ Rory > < http://ln.org/ learns_from > < http://ln.org/person/ Adriana > </li>...
SPARQL <ul><li>SELECT ?tutor </li></ul><ul><li>WHERE  </li></ul><ul><li>{ </li></ul><ul><li>< http://ln.org/person/Rory > ...
Result <ul><li>< http://ln.org/person/Adriana > </li></ul>
How can this help us? <ul><li>store learning networks data in RDF </li></ul><ul><li>use SNA to analyse network, individual...
CSCL script and roles Capuano  et al , 2011)
SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree = 5
SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<family> = 2
SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<friend> = 1
SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<peer learner> = 2
SPARQL n-degree select  ?y count(?x) as ?degree  where{ {?x $path  ?y filter(match($path, star( param[type] ))) filter(pat...
Summary <ul><li>Semantic Web and Social Network Analysis help us  make sense  of different types of data that are in a soc...
Questions? <ul><li>[email_address] </li></ul><ul><li>http://www.open.ou.nl/rse </li></ul><ul><li>openrory, maisonpoublon <...
References <ul><li>R project ( http://www.r-project.org/ ) </li></ul><ul><li>UCINET ( https://sites.google.com/site/ucinet...
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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.

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  • Thank you for your attention, and I hope to see you at the Career day
  • Social Network Analysis, Semantic Web and Learning Networks

    1. 1. LN SNA Seminar session 2 <ul><li>Adriana Berlanga & Rory Sie </li></ul>
    2. 2. Schedule! <ul><li>Session 3 ( March 13th , 13-15h, Chiba 1.42/video conference): </li></ul><ul><ul><li>Miguel-Angel Sicilia and Karina Cela (University of Alcalá) </li></ul></ul><ul><li>Session 4 (April 17th, 13-15h, Chiba 1.42): </li></ul><ul><ul><li>Yiwei Cao (RWTH Aachen) </li></ul></ul><ul><li>Session 5 (June 19th, 14-16h , Chiba 1.42/video conference): </li></ul><ul><ul><li>Julita Vassileva (video conference) </li></ul></ul>
    3. 3. SNA & Semantic Web (and LN) <ul><li>Rory Sie </li></ul>
    4. 4. Outline <ul><li>Recap </li></ul><ul><li>Semantic Web </li></ul><ul><li>Use for LN </li></ul>
    5. 5. Recap
    6. 6. Network measures <ul><li>network: density, connectivity, centralization </li></ul><ul><li>community: factions, cliques </li></ul><ul><li>individual: betweenness, degree, closeness </li></ul>
    7. 7. Network measures <ul><li>network: density, connectivity, centralization </li></ul><ul><li>community: factions, cliques </li></ul><ul><li>individual: betweenness, degree, closeness </li></ul>
    8. 8. Network measures <ul><li>network: density, connectivity, centralization </li></ul><ul><li>community: factions, cliques </li></ul><ul><li>individual: betweenness, degree, closeness </li></ul>
    9. 9. Data storage <ul><li>Adjacency matrix (R, UCINET) </li></ul><ul><li>GML/XGMML (Cytoscape, Gephi) </li></ul><ul><li>Pajek Network (Pajek, UCINET) </li></ul>
    10. 10. Data storage <ul><li>Adjacency matrix (R, UCINET) </li></ul><ul><li>GML/XGMML (Cytoscape, Gephi) </li></ul><ul><li>Pajek Network (Pajek, UCINET) </li></ul><ul><li><?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;> </li></ul><ul><li><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;/> </li></ul><ul><ul><li></edge> </li></ul></ul><ul><ul><ul><li></graph> </li></ul></ul></ul>
    11. 11. Data storage <ul><li>Adjacency matrix (R, UCINET) </li></ul><ul><li>GML/XGMML (Cytoscape, Gephi) </li></ul><ul><li>Pajek Network (Pajek, UCINET) </li></ul>
    12. 12. Analysis But what if you want to do this real-time / online?
    13. 13. CytoscapeWeb <ul><li>http://cytoscapeweb.cytoscape.org </li></ul><ul><li>Cytoscape, but online </li></ul><ul><li>Great for visualization </li></ul>
    14. 14. Connect R to web <ul><li>RemoteREngine package </li></ul>
    15. 16. Web 1.0
    16. 17. Web 2.0
    17. 18. Semantic Web (3.0) writes writes about place writes about resource
    18. 19. Semantic Web (3.0) writes writes about place writes about resource
    19. 20. Semantic Web (3.0) writes writes about place writes about resource learns from friend of mother of follows
    20. 21. learning networks
    21. 22. Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. SPARQL) </li></ul>
    22. 23. Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. SPARQL) </li></ul>“ Rory” “ learns from” “ Adriana”
    23. 24. Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. SPARQL) </li></ul>“ Rory” “ learns from” “ Adriana” subject predicate object
    24. 25. Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. SPARQL) </li></ul>subject predicate object triple “ Rory” “ learns from” “ Adriana”
    25. 26. Example data <ul><li>< http://ln.org/person/ Rory > < http://ln.org/ learns_from > < http://ln.org/person/ Adriana > </li></ul>
    26. 27. Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. SPARQL) </li></ul>
    27. 28. http://www.ag-nbi.de/research/swrlengine/
    28. 29. Knowledge Representation <ul><li>RDF </li></ul><ul><li>Triple store (e.g. Sesame) </li></ul><ul><li>Query language (e.g. SPARQL) </li></ul>
    29. 30. Example data <ul><li>< http://ln.org/person/ Rory > < http://ln.org/ learns_from > < http://ln.org/person/ Adriana > </li></ul>
    30. 31. SPARQL <ul><li>SELECT ?tutor </li></ul><ul><li>WHERE </li></ul><ul><li>{ </li></ul><ul><li>< http://ln.org/person/Rory > < http://ln.org/learns_from > ?tutor </li></ul><ul><li>} </li></ul>
    31. 32. Result <ul><li>< http://ln.org/person/Adriana > </li></ul>
    32. 33. How can this help us? <ul><li>store learning networks data in RDF </li></ul><ul><li>use SNA to analyse network, individuals, communities, topics </li></ul>
    33. 34. CSCL script and roles Capuano et al , 2011)
    34. 35. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree = 5
    35. 36. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<family> = 2
    36. 37. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<friend> = 1
    37. 38. SemWeb, LNs and SNA peer learner peer learner friend father mother adapted from Ereteo degree<peer learner> = 2
    38. 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
    39. 40. Summary <ul><li>Semantic Web and Social Network Analysis help us make sense of different types of data that are in a social network </li></ul>
    40. 41. Questions? <ul><li>[email_address] </li></ul><ul><li>http://www.open.ou.nl/rse </li></ul><ul><li>openrory, maisonpoublon </li></ul><ul><li>Rory Sie </li></ul><ul><li>openrse </li></ul><ul><li>http://nl.linkedin.com/in/rorysie </li></ul><ul><li>thebigbangrory.blogspot.com </li></ul>
    41. 42. References <ul><li>R project ( http://www.r-project.org/ ) </li></ul><ul><li>UCINET ( https://sites.google.com/site/ucinetsoftware/home ) </li></ul><ul><li>Gephi ( http://gephi.org/ ) </li></ul><ul><li>Cytoscape ( http://www.cytoscape.org ) </li></ul><ul><li>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 </li></ul><ul><li>Berners-lee, B. T., Hendler, J., & Lassila, O. (2001). The Semantic Web. Scientific American . </li></ul><ul><li>Guillaume Ereteo’s PhD defense ( http://www.slideshare.net/ereteog/phd-defense-semantic-social-network-analysis ) </li></ul><ul><li>Microformats (http://microformats.org/) </li></ul>
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