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How the Semantic Web is Being Used: An Analysis of FOAF Documents

How the Semantic Web is Being Used: An Analysis of FOAF Documents. Li Ding, Lina Zhou, Tim Finin, Anupam Joshi eBiquity Lab, Department of CSEE University of Maryland Baltimore County. Outline. Introduction The six popular ontologies FOAF vocabulary Why FOAF

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How the Semantic Web is Being Used: An Analysis of FOAF Documents

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  1. How the Semantic Web is Being Used:An Analysis of FOAF Documents Li Ding, Lina Zhou, Tim Finin, Anupam Joshi eBiquity Lab, Department of CSEE University of Maryland Baltimore County

  2. Outline • Introduction • The six popular ontologies • FOAF vocabulary • Why FOAF • Building FOAF Document collection • FOAF Document Identification • FOAF Document Discovery • Popular Properties of foaf:Person • Applications • Personal Information Fusion • Social Network Analysis

  3. The Six Most Popular Ontologies RDF DC RSS MCVB FOAF RDFS The statistics is generated by http://swoogle.umbc.edu

  4. FOAF vocabulary (http://xmlns.com/foaf/0.1/) @

  5. Why FOAF • Information Creators • Community membership management • Unique Person Identification (privacy preserved) • Indicating Authorship • Information Consumers • Provenance tracking • Social networking • Expose community information to new comers • Match interests • Trust building block

  6. Identify a FOAF document • D is a generic FOAF document when 1,2,3 met • D is a strict FOAF document when 1,2,3,4 met • D is an RDF document. • D uses FOAF namespace • The RDF graph serialized by D contains the sub-graph below • D defined one and only one master Person foaf:Person rdf:type X foaf:Y Z

  7. FOAF document Discovery • Bootstrap: using web search engine (Got 10,000 docs) • Discovery: using rdfs:seeAlso semantics (Got 1.5M docs) Top 7 FOAF websites

  8. Popular properties of foaf:Person (1/2) Top 10 popular properties (per document) *DS-FOAF-SMALL is a newly dataset in Oct 2004, based on 7276 evenly sampled documents.

  9. Popular properties of foaf:Person (2/2) Top 10 popular properties (per instance) *DS-FOAF-SMALL is a newly dataset in Oct 2004, based on 7276 evenly sampled documents.

  10. Collecting Personal Information http://www-2.cs.cmu.edu/People/fgandon/foaf.rdf http:www.cs.umbc.edu/~dingli1/foaf.rdf

  11. Caution: Collision? Mistake! caution http://www.ilrt.bris.ac.uk/people/cmdjb/webwho.xrdf http://www.mindswap.org/~katz/2002/11/jordan.foaf

  12. SNA1: Instances of foaf:Person per doc • Zipf’s distribution • Sloppy tail: few person directory documents contains thousands of instances Cumulative distribution

  13. SNA2: Instances of foaf:Person per group A group refers to a fused person • Zipf’s distribution • Sloppy tail: some instances are wrongly fused due to incorrect FOAF documents Cumulative distribution

  14. SNA3: In-degree of group • Zipf’s Distribution • Sharp tail: few FOAF documents have large in-degrees Cumulative distribution

  15. SNA4: Out-degree of group • Zipf’s distribution • Sloppy tail: few person directory documents Cumulative distribution

  16. SNA5: Patterns of FOAF Network • Four types of group • Isolated • Only in • only one inlink (97%) • Only out • Both (intermediate) • Basic Patterns: • Singleton: (isolated) • Star: (only out) an active person publishes friends • Clique: a small group

  17. SNA6: Size of components • Zipf’s distribution • Sloppy head: singleton • Sloppy tail: blog websites (e.g. www.livejournal.com) Cumulative distribution

  18. SNA7: Growth of FOAF network 3 1 2

  19. The Map of FOAF network (Jun,2004) Blog.livedoor.jp non-blog www.ecademy.com www.livejournal.com

  20. Questions? Demo: http://apple.cs.umbc.edu/semdis Swoogle: http://swoogle.umbc.edu eBiquity group: http://ebiquity.umbc.edu

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