Social Interaction Ontology

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

Transcript of Social Interaction Ontology

  • 1. Research DraftSocial Semantic Weband Social Interaction Ontology Seokchan (Channy) Yun Biomedical Knowledge Engineering Laboratory Seoul National UniversityJune 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 Milgrams six degrees of separation theory Erds number Dunbars 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 FOAFSeokchan Yun 241021fb0e6289f92815fc210f9e9137262c252e Hong-Gee Kim 13. Common Structure 14. SIOC (John Breslin) 15. Example of SIOCFirefox supports Geolocation Service2006-09-07T09:33:30ZFirefox 3.5 Beta 4 and higher starts to support W3Cs Geolocation APIs developed by Google.com based... 16. FOAF+ SIOC16 17. FOAF+SIOC+SKOS skos:isSubjectOf sioc:topic17 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. Whats Definition of Online Friends?http://answers.polldaddy.com/poll/1230119/?view=resultsWell-known Friends 9% Colleagues 7% Known Meet once in offline 25% Knowing only name 12%Famous person 3% Unknown FoaF 13% UnknownEveryone requested 32% Online Friend != RealFOAFs 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 PakistanAbdullah 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 users activities Widely supported by Facebook, MySpace etc. Basic Format User, Verb, Noun 33. Example of Activity Streamstag:photopanic.example.com,2008:activity01Geraldine posted a Photo on PhotoPanic2008-11-02T15:29:00Zhttp://activitystrea.ms/schema/1.0/posttag:photopanic.example.com,2008:photo01My Cat2008-11-02T15:29:00Z tag:atomactivity.example.com,2008:photoGeraldine's Photos

Geraldine posted a Photo on PhotoPanic

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. Whats good photos enjoying with friend? RateMDs There are many doctors. Whats 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 Twitters 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 Sitesselect 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 users reaction for alert Whom I should follow? Who is expert in Twitter? Export twitter activity to SIO Measuring coupling-degree index Evaluation with users reaction for recommendation 44. Interaction Interaction InteractionOntologyOntologyOntology ExporterExporterExporter 45. Q&Achanny@snu.ac.kr http://www.creation.netTwitter: @channyun