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The Semantic Web in use: Analyzing FOAF Documents

The Semantic Web in use: Analyzing FOAF Documents. Li Ding, Lina Zhou, Tim Finin and Anupam Joshi University of Maryland, Baltimore County. DARPA contract F30602-00-0591and NSF awards ITR-IIS-0326460 and ITR-IIS-0325464 provided partial research support for this work. Outline. Motivation

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The Semantic Web in use: Analyzing FOAF Documents

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  1. The Semantic Web in use:AnalyzingFOAF Documents Li Ding, Lina Zhou,Tim Finin and Anupam Joshi University of Maryland, Baltimore County DARPA contract F30602-00-0591and NSF awards ITR-IIS-0326460 and ITR-IIS-0325464 provided partial research support for this work

  2. Outline • Motivation • 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 Semantic Web • The semantic web vision is that information and services are described using shared ontologies in KR-like markup languages, making them accessible to machines (programs). • How do we get there? • What kind of ontologies? IEEE SUO? Cyc? • What kind of languages? RDF? OWL? RuleML? • It’s reasonable to start with the simple and move toward the complex • From Dublin Core to CYC • From RDF to OWL and beyond • Significant semantic web content exists today • Using simple vocabularies (e.g., FOAF) and RDF/RDFS

  4. The Semantic Web • The more important word in “Semantic Web” is the latter • The KR aspects of the SW were taken off the shelf, the result of 25 years of research done in the AI community • Remember hypertext? It was a nice research backwater going back to the 50’s (recall Memex and Xanadu) • Hypertext was forever change by the Web • So maybe the web will forever change KR • TBL: “The Semantic Web will globalize KR, just as the WWW globalize hypertext”

  5. Web of what? • What features does the web bring to the table? • “Anyone can say anything about anything” • The meaning of RDF terms will be (partly) determined socially • It’s a web of documents, services, agents and people

  6. What kind of Ontologies? Thesauri “narrower term” relation space of interest Disjointness, Inverse,part of… Frames (properties) Formal is-a Catalog/ID CYC DB Schema UMLS RDF RDFS DAML Wordnet OO OWL IEEE SUO Formal instance General Logical constraints Informal is-a Value Restriction Terms/ glossary Vocabularies SimpleOntologies ExpressiveOntologies Taxonomies After Deborah L. McGuinness (Stanford)

  7. The Semantic Web Today • There are several simple RDF vocabularies that are widely used today • Dublin Core • RSS • FOAF • It’s instructive to study how these are being used today • And to track how their usage changes

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

  9. A usecase: FOAF • FOAF (Friend of a Friend) is a simple ontology to describe people and their social networks. • See the foaf project page: http://www.foaf-project.org/ • We recently crawled the web and discovered over 1,500,000 valid RDF FOAF files. • Most of these are from seveal blogging system that encode basic user info in foaf • See http://apple.cs.umbc.edu/semdis/wob/foaf/ • <foaf:Person> • <foaf:name>Tim Finin</foaf:name> • <foaf:mbox_sha1sum>2410…37262c252e</foaf:mbox_sha1sum> • <foaf:homepage rdf:resource="http://umbc.edu/~finin/" /> • <foaf:img rdf:resource="http://umbc.edu/~finin/images/passport.gif" /> • </foaf:Person>

  10. FOAF vocabulary http://xmlns.com/foaf/0.1/ @

  11. FOAF: why RDF? Extensibility! • FOAF vocabulary provides 50+ basic terms for making simple claims about people • FOAF files can use other RDF terms too: RSS, MusicBrainz, Dublin Core, Wordnet, Creative Commons, blood types, starsigns, … • RDF guarantees freedom of independent extension • OWL provides fancier data-merging facilities  • Result: Freedom to say what you like, using any RDF markup you want, and have RDF crawlers merge your FOAF documents with other’s and know when you’re talking about the same entities.  After Dan Brickley, danbri@w3.org 

  12. No free lunch! Consequence: • We must plan for lies, mischief, mistakes, stale data, slander • Dataset is out of control, distributed, dynamic • Importance of knowing who-said-what • Anyone can describe anyone • We must record data provenance • Modeling and reasoning about trust is critical • Legal, privacy and etiquette issues emerge • Welcome to the real world After Dan Brickley, danbri@w3.org 

  13. FOAF example using XML <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:foaf="http://xmlns.com/foaf/0.1/"> <foaf:Person> <foaf:name>Tim Finin</foaf:name> <foaf:mbox rdf:resource="mailto:finin@umbc.edu"/> </foaf:Person> </rdf:RDF>

  14. FOAF example using XML <foaf:Person> <foaf:name>Tim Finin</foaf:name> <foaf:mbox rdf:resource="mailto:finin@umbc.edu"/> <foaf:nick>Tim</foaf:nick> <foaf:homepage rdf:resource="http://umbc.edu/~finin/"/> <foaf:img rdf:resource= "http://umbc.edu/~finin/passport.gif"/> </foaf:Person>

  15. FOAF example using XML <foaf:Person> <foaf:name>Tim Finin</foaf:name> <foaf:knows> <foaf:Person> <foaf:name>Anupam Joshi</foaf:name> <rdf:seeAlso rdf:resource = "http://umbc.edu/~joshi/joshi.foaf"/> <foaf:knows> </foaf:Person>

  16. FOAF isn’t the only one • Other ontologies are used to publish social information • Swoogle finds >360 RDFs or OWL classes with the local name “person.”

  17. Lots of FOAF tools

  18. 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

  19. Studying how FOAF is being used • What counts as a FOAF document? • How can we find foaf documents?

  20. 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 defines one and only one Person instance foaf:Person rdf:type X foaf:Y Z

  21. Different FOAF collections • DS-Swoogle • Foaf documents selected from Swoogle’s database of ~340K semantic web documents • Swoogle selects at most 1000 documents from any site • DS-FOAF • Custom crawler found 1.5M foaf documents, most from a few large blog sites (e.g., livejournal) • DS-FOAF-Small • Subset of ~7K non-blog foaf documents from ~1K sites defining ~37K people

  22. 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

  23. From DS-Swoogle • 17 SWDs add to the definition of foaf:Person • e.g., defining superclasses, disjointness, etc. • 162 properties are defined for foaf:Person • e.g., properties whose domain is foaf:Person • 74 properties defined as relations between people • e.g., properties with both domain and range of foaf:Person • 582 properties used • e.g., used to assert something of a foaf:Person instance

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

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

  26. Extracting social networks Three steps • Discovering foaf instances • Merging instances representing the same person • Linking people via foaf:knows and other foaf based relations • e.g., quaffing:drankBeerWith • Integrating other SNA data • e.g., from co-author relationships mined from citeseer

  27. Merging instances • Named instances • Inverse functional properties • Set of nearly inverse functional properties • OWL constraints • Rdf:seeAlso

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

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

  30. SNA1: Instances of foaf:Person/doc • Zipf’s distribution • Sloppy tail: few foaf documents contain thousands of instances Cumulative distribution

  31. SNA2: Instances of foaf:Person/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

  32. Degree analysis • For social networks, the in-degree and out-degree measure of a person is of interest • Can be used to identify hubs and authorities or to compute other interesting properties or rankings • Analyzing most large social networks reveals that in-degree and out-degree follows a power law or Zipf distribution • We found that to be the case for social networks induced by foaf documents.

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

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

  35. 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

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

  37. SNA7: Growth of FOAF network The data suggests that there is a natural evolution for a social network (1) disjointed star-like, connected components (2) link together to form trees and forests, (3) eventually forming a scale-free network

  38. SNA7: Growth of FOAF network 3 1 2

  39. The Map of FOAF network Blog.livedoor.jp non-blog www.ecademy.com June 2004 www.livejournal.com

  40. Conclusions • The semantic web is evolving • There is a growing volume of RDF content • FOAF is one of the one of the early successes. • FOAF data is being used • FOAF data is relatively easy to collect and analize • FOAF data is a good source for social network information

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

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