1.
Research Draft
Social Semantic Web
and Social Interaction Ontology
Seokchan (Channy) Yun
Biomedical Knowledge Engineering Laboratory
Seoul National University
June 8th 2009

2.
Agenda
• Online Social Network
– Traditional research
• Social Semantic Web
– FOAF/SIOC
– Tripartite Social Ontology
– Social Semantic Network
• Activity-based Approach
– Online Presence Project
– Activity Streams
• Social Interaction Ontology
– Research Plan

3.
Social Networks in Socialogy
• Social Network used firstly
J.A. Barnes (1954)
• Research methodology in
Social Sciences
• Generally sameAs Network
(Graph) Theory
– Milgram’s six degrees of
separation theory
– Erdős number
– Dunbar’s Number – 150
(average 124)
– Diffusion of innovations

4.
Representation of Social Network
• Graph Model with Node and Edge

5.
Emerging Online Social Network
• New opportunities for social science
– Explicit and implicit social network information
– Large scale data sets
– Dynamic data
– Different modalities (profiles, email, IM, Twitter…)
• Challenges
– Theoretical
– Friend on the Web = Friend in reality?
– Extracting information
– Heterogeneity
– Quality of data
– Time and space complexity
– Ethical and legal challenges
• Semantic technologies can help with some of the technical challenges

6.
History
• First Mover
– Classmates.com,
Match.com and
sixdegree.com
– Friendster and Orkut
• Majority
– Myspace and Facebook
– Linkedin.com
– Twitter.com

7.
Classification

8.
How succeed?
• Allows a user to create and maintain an online network of
close friends or business associates for social and
professional reasons:
– Friendships and relationships
– Offline meetings
– Curiosity about others
– Business opportunities
– Job hunting
• Allows a user to share interests based on object-centered
sociality with meaning
– Sharing photo, video and bookmark
– Life streaming over SNS
– Broadcasting and publishing of my own content

9.
Status of Online SNS
John Breslin, The Social Semantic Web: An Introduction (2009)

10.
Social Semantic Information Spaces

11.
FOAF

12.
Example of FOAF
<foaf:Person>
<foaf:name>
Seokchan Yun
</foaf:name>
<foaf:mbox_sha1sum>
241021fb0e6289f92815fc210f9e9137262c252e
</foaf:mbox_sha1sum>
<foaf:homepage rdf:resource=quot;http://www.creation.netquot; />
<foaf:knows>
<foaf:Person>
<foaf:name>Hong-Gee Kim</foaf:name>
<foaf:mbox rdf:resource=”mailto:hgkim@snu.ac.kr”/>
<rdfs:seeAlso rdf:resource=“http://.../foaf.rdf />
</foaf:Person>
</foaf:Person>

13.
Common Structure

14.
SIOC (John Breslin)

15.
Example of SIOC
<sioc:Post rdf:about=quot;http://koreacrunch.com/archive/firefox-supports-geolocation-servicequot;>
<dcterms:title>Firefox supports Geolocation Service</dcterms:title>
<dcterms:created>2006-09-07T09:33:30Z</dcterms:created>
<sioc:has_container rdf:resource=quot;http://www.creation.net/?sioc_type=site#weblogquot;/>
<sioc:has_creator>
<sioc:User rdf:about=quot;http://www.creation.net/quot; rdfs:label=quot;Cloudquot;>
</sioc:User>
</sioc:has_creator>
<sioc:content>Firefox 3.5 Beta 4 and higher starts to support W3C’s Geolocation APIs
developed by Google.com based...
</sioc:content>
<sioc:topic rdfs:label=quot;Firefoxquot; rdf:resource=quot;http://koreacrunch.com/tags/firefox/quot;/>
<sioc:topic rdfs:label=quot;Geolocationquot; rdf:resource=quot;http://koreacrunch.com/tags/...quot;/>
<sioc:has_reply>
<sioc:Post rdf:about=quot;http://koreacrunch.com/archive/351#comment-123928quot;>
</sioc:Post>
</sioc:has_reply>
</sioc:Post>

16.
FOAF+ SIOC
16

17.
FOAF+SIOC+SKOS
skos:isSubjectOf
sioc:topic
17

18.
Disconnected sites on the Social Web

19.
Tripartite Social Ontology (Peter Mica)
• A graph model of ontologies based on tripartite
graphs of actors, concepts and instances
– Actors: users
– Concepts: tags
– Instances: objects
• Emergent semantics
– General idea: observe semantics in the way agents interact
(use concepts)
• Bottom-up ontologies
• Semantics = syntax + statistics

20.
Dimensional Ontology Oci and Oac

21.
e.g. Delicious
• When looking at co-occurrence of terms (Oci )
– Network reflects language use
– Better for clustering, determining ambiguity of terms and
finding synonyms
• E.g. travel - cote, provence, villa, azur, mas, holiday, vacation,
tourism, france, heritage
• When looking at community overlaps (Oac )
– Network reflects the domain
– Better for finding broader/narrower terms, non-trivial
relationships
• E.g. google – gmail, picasa, youtube…

22.
e.g. Flink

23.
Social Semantic Network (Jason Jung)

24.
Remained Question?

25.
What’s Definition of Online Friends?
http://answers.polldaddy.com/poll/1230119/?view=results
Well-known Friends 9%
Colleagues 7%
Known
Meet once in offline 25%
Knowing only name 12%
Famous person 3%
Unknown FoaF 13% Unknown
Everyone requested 32%
Online Friend != Real
FOAF’s knows is not knowing!

26.
Twitter

27.
Facebook

28.
me2DAY

29.
LinkedIn

30.
Challenges
• Discovering Knowledge
Abdullah Al Reshood roommateOf Aafia Siddique
Aafia Siddique isMemberOf Al Queda
Aafia Siddique traveledTo Pakistan
Mounir-al Motassad traveledTo Pakistan
Abdullah Al Reshood isRelatedTo Al Queda ?
Al Queda isRelatedTo Pakistan ?
• Degree of User relationship
– Coupling between users (high vs. weak)
– Removing coupling bias in analysis

31.
Online Presence Project (Milan Stankovic)
• Feel of Presense
– Status Messages
– Online Status (Busy, Available, Away…)
– Current listening music, activities…

32.
Activity Streams (Chris Messina)
• Lightweight simple Atom based syndication for user’s
activities
• Widely supported by Facebook, MySpace etc.
• Basic Format
– User, Verb, Noun

33.
Example of Activity Streams
<entry>
<id>tag:photopanic.example.com,2008:activity01</id>
<title>Geraldine posted a Photo on PhotoPanic</title>
<published>2008-11-02T15:29:00Z</published>
<link rel=quot;alternatequot; type=quot;text/html“ href=quot;/geraldine/activities/1quot; />
<activity:verb>
http://activitystrea.ms/schema/1.0/post
</activity:verb>
<activity:object>
<id>tag:photopanic.example.com,2008:photo01</id>
<title>My Cat</title>
<published>2008-11-02T15:29:00Z</published>
<link rel=quot;alternatequot; type=quot;text/html“ href=quot;/geraldine/photos/1quot; />
<activity:object-type>
tag:atomactivity.example.com,2008:photo
</activity:object-type>
<source>
<title>Geraldine's Photos</title>
<link rel=quot;selfquot; type=quot;application/atom+xml“ href=quot;/geraldine/photofeed.xmlquot; />
<link rel=quot;alternatequot; type=quot;text/html“ href=quot;/geraldine/quot; />
</source>
</activity:object>
<content type=quot;htmlquot;>
&lt;p&gt;Geraldine posted a Photo on PhotoPanic&lt;/p&gt;
&lt;img src=quot;/geraldine/photo1.jpgquot;&gt;
</content>
</entry>

34.
Other approaches

35.
Questions
• Twitter
– There are many spammers and followers.
– Whom I should follow? Who is expert?
• me2DAY
– There are many friends
– Who disconnected in my friendship?
• Flickr
– There are many photos.
– What’s good photos enjoying with friend?
• RateMDs
– There are many doctors.
– What’s good doctors recommended by friends?

36.
Answers
• 1. Measuring degree of user relationship
– Twitter: Following < RT < Reply < Direct Message < SMS
– me2Day: me2 < Link < Reply < Memo < Gift < SMS
– Exporting Social Interaction Ontology
• 2. Modeling of user degree
– Similarity formula (A,B)
• 3. Integration data for answer

37.
Pre-work for solving problem
• Social web evolves direct sharing and
broadcasting instead of document link based
distribution and knowledge discovering.
– Social Interaction is more important in social networks.
– FriendFeed, Facebook life streaming, Twitter
• Need to represent “Degree between people”
– Writing simple ontology represents interaction
• Channy replies Hong-Gee (What) (When) in Facebook
• John retweets Channy (What) (When) in Twitter

38.
Why new ontology?
• SIOC
– Only focusing on relationship with site(forum), contents
and person.
• OPO
– Only focusing “Presence” not to be interested in “Activity”
– No description on various interaction such as Twitter’s RT
– Need to more practical
• Activity streams
– Atom (RDF) based
– Only description for Person / Verb / Object

39.
Twitter interaction

40.
Facebook interaction

41.
me2DAY interaction

42.
Social Interaction Ontology
• Focusing on User-to-User
– User / Verb / User in Sites select count(?y) as ?cdegree
{
• E.g. fromAccount Act toAccount { ?y interAct:retweet ?x}
UNION
{?x interAct:directmessage ?y}
• Connection with FOAF and SIOC }
group by ?x
– foaf:Person
– foaf:holdsAccount
• sioc:User rdfs:subClassOf foaf:onlineAccount
– interAct:fromAccount rdfs:sioc:User
• Expression for various interaction
– Verb : comment, reply, me2, RT, hashtag…
• E.g act:comment sioc:Sites

43.
Research Plan
• Who disconnected in my friendship on me2DAY?
– Modeling Social Interactive Ontology
– Export me2DAY activity to SIO
– Measuring coupling-degree index
• Distance = # of interaction/ time interval
• Priority = normalized value for each interactions
– Evaluation with user’s reaction for alert
• Whom I should follow? Who is expert in Twitter?
– Export twitter activity to SIO
– Measuring coupling-degree index
– Evaluation with user’s reaction for recommendation

44.
Interaction Interaction Interaction
Ontology Ontology Ontology
Exporter Exporter Exporter

45.
Q&A
channy@snu.ac.kr
http://www.creation.net
Twitter: @channyun