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6. Browsing, Exploring and Consuming Semantic Web Data

6. Browsing, Exploring and Consuming Semantic Web Data. Sheila Kinsella. Consumers of Social Semantic Data. Collectors. Consumers. Producers. Add-Ons and Fns. RDF Browsers. RDF Crawlers. and Other Custom. and Smushing. for Exporting RDF. Social Explorers. of Data. from Social Apps.

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6. Browsing, Exploring and Consuming Semantic Web Data

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  1. 6. Browsing, Exploring and Consuming Semantic Web Data Sheila Kinsella

  2. Consumers of Social Semantic Data Collectors Consumers Producers Add-Ons and Fns RDF Browsers RDF Crawlers and Other Custom and Smushing for Exporting RDF Social Explorers of Data from Social Apps Infer Relationships Querying and RDF from Semi- from Aggregate Answering Qns Structured Data RDF Data Based on Sem Nets or Web APIs Social Sites with Reuse and Import Native Storage of for Data Portability RDF Data Requirements Analysis and Bypassing Apps by Food Chain Visualisation of Directly Mapping of Semantic Data from Semantic Networks RDBMS to RDF Social Networks and Social Media Sites

  3. Consuming Social Semantic Web data • FOAF, SIOC etc. = RDF data • Generic Semantic Web applications can be used: • RDF APIs (Jena, Redland, etc.) • RDF crawlers • RDF browsers (Tabulator, Zitgist, SIOC RDF Browser, etc.) • More apps: http://www.w3.org/2001/sw/SW-FAQ#tools • Customised applications can provide more added value and/or better user interfaces

  4. Applications for browsing the social (semantic) graph • FOAFnaut, FOAF Explorer, etc. • FOAFGear: thanks to common semantics, only 100 lines of code: http://apassant.net/home/2008/01/foafgear/

  5. Aggregation of semantic social networks • Browse / re-use your social graph in personal applications • Merge identities with pre-defined rules • Tools: • Beatnik • Knowee • SPARQLpress • Nepomuk (Social Semantic Desktop)

  6. How can SIOC data be used?

  7. SIOC RDF Browser http://sparql.captsolo.net/browser

  8. SIOC Store Browser http://apassant.net/home/2006/06/sioc-browser

  9. SIOC Store Browser (2)

  10. Accessing SIOC content from multiple sources Browsing SIOC content from one source SIOC Explorer - Filter by “facet” from all sources • Facet can be a direct or indirect property: Direct • The topic of the content item • The creator of the item • The date created … Indirect • A geographic location of the person who created it • The gender of the person • An interest shared by many creators

  11. Exploring implicit social network connections

  12. Social SIOC Explorer

  13. Browsing SIOC with Piggy Bank

  14. Browsing SIOC with Tabulator

  15. Browsing SIOC with TimeLine

  16. Browsing SIOC with TimeLine (2)

  17. Browsing SMOB

  18. Reviews using SW technologies • Revyu: • http://revyu.com • Review website combining Web 2.0 / SW technologies

  19. People are represented as nodes or “actors” Relationships are represented as lines or edges: Relationships may be acquaintanceship, friendship, co-authorship, etc. Allows analysis using tools of mathematical graph theory, and mapping: Movie actors Scientists and mathematicians Sexual interaction Phone call patterns What is social network analysis?

  20. Centrality measures • Identify the most strategically located actors • Example: Kite Network, developed by David Krackhardt • Links indicate interactions

  21. Degree centrality • Based on number of direct ties an actor has • Locates “connectors”

  22. Closeness centrality • Based on how close an actor is to all other actors • Locates actors with good visibility

  23. Betweenness centrality • Based on the number of shortest paths an actor lies on • Locates actors who act as bridges between communities

  24. Cohesive subgraphs • Subset of actors who are more closely and intensely linked to each other than the rest of the network • Important for understanding behaviour of actors • Investigate why certain actors cluster together • Actors are influenced by their communities • Can be located based on • Mutuality of ties • Reachability of members • Frequency of ties

  25. Clique • Set of nodes where each node is connected to all others • At least 3 actors

  26. n-clique • Subgraph of nodes that are all within distance n of each other • Distance aspect less restrictive than clique {a,b,c,e,f} is a 2-clique

  27. k-core • Subgraph where each node has a tie to at least k others {a,b,d,e} is a 2-core

  28. Visualising a social network • Can enable an improved insight into the structure of a graph • May help analysts to understand the network better themselves, and also aid in explaining features of the network to others

  29. Vizster, based on Prefuse

  30. Free tools for social network analysis/visualisation • Pajek • http://vlado.fmf.uni-lj.si/pub/networks/pajek/ • JUNG • http://jung.sourceforge.net/ • Prefuse • http://prefuse.org/

  31. Individuals are revealing more and more information on SNS and other social software sites Advertisers and marketers can gain better understanding from customer behavioural patterns NSA using social network analysis technologies for homeland security Personal privacy issues, where sensitive personal information is revealed on SNS Analysing masses of social network information, “clouds” showing the overall picture NSA also using “automated intelligence profiling” based on unreliable information Knowing too much?

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