Ehsan Zamiri Supervisor: Dr. Kahani Ferdowsi University of Mashad FOAF: Semantic Based NameSpace for Social Networking

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

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

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”

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

What kind of Ontologies? General Logical constraints Terms/ glossary Thesauri “narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restriction Disjointness, Inverse, part of… Taxonomies Wordnet CYC RDFDAML OO DB SchemaRDFS IEEE SUOOWL UMLS Vocabularies Simple Ontologies Expressive Ontologies

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

The Six Most Popular Ontologies includes the terms necessary for describing vocabularies RDF DC RSS FOAF RDFS MCVB The statistics is generated by

A usecase: FOAF FOAF (Friend of a Friend) is a simple ontology to describe people and their social networks. See the foaf project page: CS Department of UMBC 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 Tim Finin 2410…37262c252e \\ cryptographical hash of address

FOAF vocabulary

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.

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

FOAF example using XML <rdf:RDF xmlns:rdf=" syntax-ns#" xmlns:foaf=" Tim Finin

FOAF example using XML (Cont’d) Tim Finin Tim

FOAF example using XML (Cont’d) Tim Finin Anupam Joshi

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.”

Lots of FOAF tools

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

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

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

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

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

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

Popular properties of foaf:Person non-blog (26,936) liveJournal.com (20,298,073) DS-FOAF-SMALL * (33,790) 1foaf:mbox_sha1sum (0.84)foaf:mbox_sha1sum (1.0)foaf:name(0.80) 2foaf:homepage (0.66 )dc:description(1.0)foaf:mbox_sha1sum(0.71) 3foaf:name (0.64)dc:title (1.0)foaf:nick (0.51) 4foaf:nick (0.61)foaf:nick (1.0)foaf:homepage (0.40) 5foaf:weblog (0.60)foaf:page (1.0)foaf:depiction (0.35) 6foaf:knows (0.44)foaf:weblog (0.99)foaf:weblog (0.30) 7foaf:mbox (0.38)rdfs:seeAlso (0.85)foaf:knows (0.28) 8foaf:img (0.38)foaf:knows (0.85)foaf:surname (0.27) 9bio:olb (0.35)foaf:dateOfBirth (0.71)foaf:firstName (0.26) 10rdfs:seeAlso (0.34) foaf:interest (0.67)rdfs:seeAlso (0.26) 11foaf:mbox (0.26) *DS-FOAF-SMALL is a newly dataset in Oct 2004, based on 7276 evenly sampled documents. Top 10 popular properties (per document)

Popular properties of foaf:Person non-blog (26,936) liveJournal.com (20,298,073) DS-FOAF-SMALL * (33,790) 1 foaf:name (0.84)dc:title (1.74)foaf:name(0.69) 2 foaf:knows (0.79)foaf:interest (1.68)foaf:mbox_sha1sum(0.65) 3 foaf:homepage (0.63)foaf:nick (1.04)rdfs:seeAlso (0.39) 4 foaf:mbox_sha1sum (0.51)foaf:weblog (1.00)foaf:nick (0.26) 5 rdfs:seeAlso (0.40)rdfs:seeAlso (0.99)foaf:homepage (0.18) 6 dc:title (0.31)foaf:knows (0.95)foaf:mbox (0.15) 7 foaf:nick (0.22)foaf:page (0.95)foaf:weblog (0.15) 8 foaf:weblog (0.18)dc:description (0.046)foaf:firstName (0.11) 9 foaf:mbox (0.15)foaf:mbox_sha1sum (0.046)foaf:surname (0.11) 10 daml:equivalentTo (0.13)foaf:dateOfBirth (0.046)foaf:depiction (0.10) 11 foaf:knows (0.07) Top 10 popular properties (per instance)

Collecting Personal Information

Caution: Collision? Mistake! caution

Instances of foaf:Person per doc Zipf’s distribution Sloppy tail: few foaf documents contain thousands of instances

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

In-degree of group Zipf’s Distribution Sharp tail: few FOAF documents have large in-degrees

Out-degree of group Zipf’s distribution Sloppy tail: few person directory documents

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

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

Growth of FOAF network

The Map of FOAF network

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 analyze FOAF data is a good source for social network information

Thanks for your attention