Research report for social interaction ontology - including activity streams, online presence project, SIOC and FOAF.

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…

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

34. 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>

35. Other approaches

36. 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?

37. 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

38. 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

39. 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

40. Twitter interaction

41. Facebook interaction

42. me2DAY interaction

43. 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

44. 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

45. Interaction Interaction Interaction
Ontology Ontology Ontology
Exporter Exporter Exporter

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