Ehsan Zamiri Supervisor: Dr. Kahani Ferdowsi University of
Mashad FOAF: Semantic Based NameSpace for Social Networking
Slide 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
Slide 3
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? Its 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
Slide 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 50s
(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
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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 Its a web of documents,
services, agents and people
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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
Slide 7
The Semantic Web Today There are several simple RDF
vocabularies that are widely used today Dublin Core RSS FOAF Its
instructive to study how these are being used today And to track
how their usage changes
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The Six Most Popular Ontologies includes the terms necessary
for describing vocabularies RDF DC RSS FOAF RDFS MCVB The
statistics is generated by http://swoogle.umbc.edu
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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/ 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 http://apple.cs.umbc.edu/semdis/wob/foaf/ Tim Finin
241037262c252e \\ cryptographical hash of email address
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FOAF vocabulary http://xmlns.com/foaf/0.1/
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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 others
and know when youre talking about the same entities.
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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
Slide 13 Tim Finin"> Tim Finin"> Tim Finin" title="FOAF
example using XML Tim Finin">
FOAF example using XML Tim Finin
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FOAF example using XML (Contd) Tim Finin Tim
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FOAF example using XML (Contd) Tim Finin Anupam Joshi
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FOAF isnt the only one Other ontologies are used to publish
social information Swoogle finds >360 RDFs or OWL classes with
the local name person.
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Lots of FOAF tools
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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
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Studying how FOAF is being used What counts as a FOAF document?
How can we find foaf documents?
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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
Slide 21
Different FOAF collections DS-Swoogle Foaf documents selected
from Swoogles 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
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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
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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
Slide 24
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)
Instances of foaf:Person per doc Zipfs distribution Sloppy
tail: few foaf documents contain thousands of instances
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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
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In-degree of group Zipfs Distribution Sharp tail: few FOAF
documents have large in-degrees
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Out-degree of group Zipfs distribution Sloppy tail: few person
directory documents
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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
Slide 33
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
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Growth of FOAF network
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The Map of FOAF network
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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
