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About the Social Semantic Web

Talk given at the Semantic Web SIKS course 2011: why we need semantics on the Social Web. Three examples: social tagging, user profiling based on Twitter streams and cross-system user profiling (linking user profiles).

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About the Social Semantic Web

  1. 1. Social Semantic WebWhy we need semantics on the Social Web<br />Somewhere, Netherlands, September 27, 2011<br />Fabian Abel<br />Web Information Systems, TU Delft<br />
  2. 2. The Social Web<br />Social Web stands for the culture of participation on the Web.<br />
  3. 3. Power-law of participation by Ross Mayfield 2006<br />
  4. 4. The Social Web<br />“Problem”<br />The Social Web is made by people for people<br />
  5. 5. Why do we need semantics on the Social Web? (from an engineering point of view)<br />Applications<br />…that understand and leverage Social Web data<br />Semantic Enrichment, Linkage and Alignment<br />user/usage data<br />Social Web<br />
  6. 6. About this talk<br />Applications<br />…that understand and leverage Social Web data<br />User Modeling and Personalization<br />Mapping words to <br />ontological concepts<br />Semantic Enrichment, Linkage and Alignment<br />Social tagging<br />Micro-blogging<br />user/usage data<br />Social Web<br />
  7. 7. Social Tagging<br />Semantics in Social Tagging Systems<br />
  8. 8. Social Tagging Systems<br />jazzmusic<br />armstrong<br />trumpet<br />baker, trumpet<br />Users<br />trumpet<br />Tags<br />armstrong, baker, dizzy, <br />jazzmusic, jazz, trumpet<br />dizzy, jazz<br />armstrong<br />Resources<br />tag<br />user<br />resource<br />Folksonomy:<br /><ul><li>set of tag assignments
  9. 9. Formal model [Hotho et al. ‘07]:</li></ul>F = (U, T, R, Y)<br />tag assignment<br />
  10. 10. Folksonomy Graph<br />A folksonomy (tag assignments) can be represented via an undirected weighted tripartite graph GF = (VF, EF) where:<br />VF = U U T U R is the set of nodes<br />EF = {(u,t), (t,r), (u,r) | (u,t,r) in Y} is the set of edges<br />
  11. 11. How to weigh the edges of a folksonomy graph?<br />w(t1, r1) = ?<br />w(t1, r1) = 2<br />tag assignments: (u1, t1, r1), (u2, t1, r1), (u2, t2, r2)<br />w(u1, t1) = ?<br />w(u1, t1) = 1<br />u1<br />w(u2,r2) = ?<br />w(u2,r2) = 1<br />t1<br />r1<br />w(t1, r1)<br />w(u,t) = ?<br />w(u,r) = ?<br />w(t,r) = ?<br />w(u1, t1)<br />t2<br />r2<br />u2<br />w(u2, r2)<br />For example: <br />w(t,r) = {u in U| (u, t, r) in Y} = count the number of users who assigned tag t to resource r<br />
  12. 12. Search & Ranking in Folksonomies<br />FolkRank[Hotho et al. 2006] is an application of PageRank[Page et al. 98] for folksonomies:<br />FolkRank-based rankings:<br /> users tags resources<br />1.<br />2.<br />preference <br />vector<br />FolkRank vector<br />adjacency matrix models the folksonomy graph<br />influence of preferences<br />t1<br />u2<br />r1<br />r2<br />t2<br />u1<br />u1 u2 t1 t2 r1 r2<br />u1 0.5 0.5<br />u2 0.25 0.25 0.25 0.25<br />t1 0.25 0.25 0.5<br />t2 0.5 0.5<br />r1 0.25 0.25 0.5<br />r2 0.5 0.5<br />u1<br />u2<br />t1 <br />t2<br />r1<br />r2<br />u1<br />u2<br />t1 <br />t2<br />r1<br />r2<br />0<br />0<br />1<br />0<br />0<br />0<br />0.1<br />0.2<br />0.3<br />0.1<br />0.3<br />0.1<br />u1<br />u1<br />t1<br />r1<br />t1<br />r1<br />t2<br />A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In Proc. ESWC, volume 4011 of LNCS, pages 411–426, Budva, Montenegro, 2006. Springer.<br />r2<br />u2<br />
  13. 13. Problems of traditional folksonomies<br />no tags<br />jazzmusic<br />armstrong<br />trumpet<br />baker, trumpet<br />trumpet<br />Tags<br />armstrong, baker, dizzy, <br />jazzmusic, jazz, trumpet<br />dizzy, jazz<br />armstrong<br />ambiguity<br />of tags<br />synonyms<br />
  14. 14. “Metadata” in Folksonomies<br />Resource Y<br />created: 1979-12-06<br />creator: …<br />metadata<br />metadata<br />metadata<br />metadata<br />User X<br />Age: 30 years<br />Education: …<br />music <br />jazz<br />Jazz (noun) is a <br />style of music that…<br />jazz<br />tag<br />User X<br />user<br />resource<br />TAS XY<br />created: 2011-04-19<br />meaning: dbpedia:Jazz<br />Metadata-enabled folksonomy:<br />Fc = (U, T, R, Y, M, Z)<br /><ul><li>M is the actual metadata information
  15. 15. Z Y xM is the set of metadata assignments</li></ul>tag assignment<br />
  16. 16. Exploiting Metadata in Folksonomies<br />DBpedia-based FolkRank can improve search performance, e.g. for Flickr images ESWC ‘10<br />r1<br />meaning:<br />dbpedia:Jazz<br />jazz<br />r2<br />meaning:<br />dbpedia:Jazz<br />jazzmusic<br />Using FolkRank to search for resources related to jazz:<br /> … dbpedia:Jazz... <br />...<br />r1 1<br />r2 1<br />... <br /> … jazz jazzmusic ... <br />...<br />r1 1<br />r2 1<br />... <br />FolkRank’s adjacency matrix:<br />
  17. 17. Representing Tagging Activities in RDF<br />& MOAT extension<br />armstrong<br />fabian<br /><br />moat:tagMeaning <><br />Representation of tag assignment via Tag Ontology:<br /><><br /> a tag:RestrictedTagging;<br />tag:taggedResource <>;<br />foaf:maker <>; <br />tag:associatedTag <>;<br /> .<br />Tag ontology:<br />MOAT:<br />
  18. 18. Pointers<br />RDF vocabularies: <br />Tag ontology:<br />MOAT:<br />SCOT:<br />Tagging datasets:<br />ICWSM ‘10 Tutorial on Social Semantic Web:<br />NER tools: DBpedia spotlight, Alchemey, OpenCalais, Zemanta,…<br />Papers:<br />Folksonomy Model and FolkRank: Hotho et al.: Information retrieval in folksonomies: Search and ranking. ESWC 2006. <br />MOAT framework: A. Passant: Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data. LDOW 2008.<br />Learning semantics from social tagging: <br />Marinho et al.: Folksonomy-based Collabulary Learning. ISWC 2008.<br />Hotho et al.: Emergent Semantics in BibSonomy. LNI vol. 94, 2006.<br />P. Mika: Ontologies are us: A unified model of social networks and semantics. Web Semantics vol. 5(1), 2007.<br />
  19. 19. Micro-blogging<br />Making sense of micro-blogging data<br />
  20. 20. Challenge: inferring interests from tweets<br />Personalized News Recommender<br />I want my personalized news recommendations!<br />Profile<br />Analysis and <br />User Modeling<br />?<br />(How) can we infer a Twitter-based user profile that supports the news recommender?<br />Semantic Enrichment, Linkage and Alignment<br />
  21. 21. 1. Profile Type<br />1. What type of concepts should represent “interests”?<br />Francesca <br />Schiavone<br />T<br />Sport<br />Francesca Schiavone French Open<br />#fo2010<br />Profile?<br />concept weight<br />Francesca Schiavone won French Open #fo2010<br />#<br />hashtag-based<br />?<br />entity-based<br />French<br />Open<br />T<br />topic-based<br />#<br />fo2010<br />time<br />June 27<br />July 4<br />July 11<br />
  22. 22. Performance of User Modeling strategies<br />Profile Type<br />Topic-based strategy improves S@10 significantly<br />#<br />Entity-based strategy improves the recommendation quality significantly (MRR & S@10)<br />T<br />
  23. 23. 2. Temporal Constraints<br />2. Which tweets of the user should be analyzed?<br />(a) time period<br />?<br />(b) temporal patterns<br />Profile?<br />concept weight<br />end<br />start<br />weekends<br />Morning:<br />Afternoon:<br />Night:<br />time<br />June 27<br />July 4<br />July 11<br />
  24. 24. Temporal patterns of user profiles<br />Temporal Constraints<br />2<br />1. Weekend profiles differ significantly from weekday profiles<br />2. the difference is stronger than between day and night profiles <br />weekday vs. weekend profiles<br />d1(pweekday, pweekend)<br />day vs. night profiles<br />d1(pday, pnight)<br />topic-based user profiles<br />
  25. 25. Impact of temporal constraints<br />Temporal Constraints<br />startcomplete<br />startfresh<br />end<br />Adapting to temporal context helps?<br />Selection of temporal constraints depends on type of user profile. <br /><ul><li>Topic-based profiles: </li></ul>adapting to temporal <br /> context is beneficial<br /><ul><li>Entity-based profiles:</li></ul> long-term profiles <br /> perform better<br />Recommendations = ?<br />yes<br />T<br />time<br />no<br />complete: 2 months<br />fresh: 2 weeks<br />end<br />start<br />yes<br />weekends<br />T<br />Recommendations = ?<br />no<br />time<br />
  26. 26. 3. Semantic Enrichment<br />3. Further enrich the semantics of tweets?<br />Francesca <br />Schiavone<br />Francesca wins French Open<br />Thirty in women's<br />tennis is primordially<br />old, an age when<br />agility and desire<br />recedes as the …<br />(a) tweet-based<br />Profile?<br />concept weight<br />Francesca Schiavone<br />French Open<br />Francesca Schiavone won!<br />Tennis<br />French <br />Open<br />(b) further enrichment<br />Tennis<br />
  27. 27. 3. Semantic Enrichment<br />More distinct topics per profile<br />further enrichment<br />(e.g. exploiting links)<br />further enrichment<br />(e.g. exploiting links)<br />More distinct entities per profile<br />Exploiting external resources allows for significantly richer user profiles (quantitatively)<br />Tweet-based<br />Tweet-based<br />entity-based user profiles<br />topic-based user profiles<br />Impact of Semantic Enrichment<br />
  28. 28. Impact of Semantic Enrichment<br />3. Semantic Enrichment<br />T<br />Tweet-based<br />Further enrichment<br />Further semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles!<br />
  29. 29. How to weights the concepts?<br />4. Weighting Scheme<br />Based on concept occurrence frequency (CF)<br />?<br />Francesca Schiavone<br />4<br />Profile?<br /> concept weight<br />3<br />French Open<br />6<br />Tennis<br />CF<br />Time Sensitive<br />weight(FrancescaSchiavone)<br />weight(French Open)<br />CF*IDF<br />weight(Tennis)<br />time<br />June 27<br />July 4<br />July 11<br />
  30. 30. Impact of weighting scheme<br />4. Weighting Scheme<br />Time-sensitive weighting functions perform best (for news recommendations)<br />time sensitive<br />not time sensitive<br />
  31. 31. Observations<br />Profile type:<br />Semantic profiles (entity-based and topic-based) perform better than hashtag-based profiles<br />Temporal Constraints: <br />Adapting to temporal context (e.g. weekend patterns) makes sense if it does not cause sparsity problems<br />Semantic Enrichment:<br />Further semantic enrichment improves profile/recommendation quality<br />Weighting Scheme:<br />Time-sensitive weighting functions allow for best news recommendation performance<br />
  32. 32. Pointers<br />Related papers, datasets & code:<br />ESWC 2011 workshop on “Making Sense of Microposts”:<br />Special Issue at Semantic Web Journal: (deadline: Nov 15) <br />
  33. 33. Linking Social Data<br />Cross-system User Modeling<br />
  34. 34. profile<br />?<br />Hi, I have a <br />new-user problem!<br />profile<br />Hi, I’m back and<br />I have new <br />interests.<br />Hi, I don’t know <br />that your <br />interests changed!<br />Pitfalls of today’s Web Systems<br />Hi, I’m your new <br />user. Give me <br />personalization!<br />System A<br />System D<br />System C<br />System B<br />How can we tackle these problems?<br />profile<br />profile<br />profile<br />time<br />
  35. 35. Cross-system user modeling on the Social Web <br />User data on the Social Web<br />
  36. 36. SocialGraph API<br />1. get other accounts <br />of user <br />Account Mapping<br />2. aggregate <br />public profile <br />data <br />Social Web Aggregator<br />Blog posts:<br />Semantic Enhancement<br />Profile Alignment<br />Bookmarks:<br />3. Map profiles to<br />target user model<br />4. enrich data with<br />semantics <br />Other media:<br />WordNet®<br />Social networking profiles:<br />FOAF<br />vCard<br />Interweaving public user data<br />Aggregated, <br />enriched profile<br />(e.g., in RDF or vCard)<br />Google Profile URI <br /><br />Analysis and user modeling<br />5. generate user profiles<br />
  37. 37. Analysis: form-based profiles<br />338 users with filled form-based profiles at the five different services.<br />2. Benefits of Profile Aggregation:<br />a. more profile attributes<br />b. more complete profiles<br />
  38. 38. Overlap of tag-based profiles<br />Overlap of tag-based profiles is less than 10% for more than 90% of the users<br />
  39. 39. Cold-start: Recommending tags / bookmarks<br />Hi, I’m your new <br />user. Give me <br />personalization!<br />delicious<br />profile<br />profile<br />?<br />user’s tags and bookmarks<br />profile<br />Ground truth:<br />leave-n-out evaluation<br />tags to explore<br />Cosine-based<br />recommender<br />Web sites to <br />bookmark<br />Cross-system<br />user modeling<br />actual tags and bookmarks of the user<br />How does cross-system user modeling impact the recommendation quality (in cold-start situations)?<br />
  40. 40. Bookmark Recommendations<br />Cross-system user modeling achieves significant improvements for cold-start bookmark recommendations<br />Twitter is a more appropriate source than Flickr<br />baseline<br />Cross UM<br />Cross UM<br />
  41. 41. Tag Recommendations over time<br /> Consideration of external <br /> profile information (Mypes) <br /> also leads to significant <br /> improvement when the <br /> profiles in the target service <br /> are growing. <br />Baseline <br />(target profile)<br />
  42. 42. Observations<br />Aggregating Social Profile Data leads to tremendous (and significant) improvements of tag and bookmark recommendation quality in cold-start situations and beyond<br />To optimize the performance one has to adapt the cross-system strategies to the concrete application setting<br />
  43. 43. Pointers<br />Workshop series on “Social Data on the Web”:<br />RDF vocabularies:<br />SIOC:<br />FOAF:<br />Weighted Interest Vocabulary:<br />Papers:<br />Abel et al.: Cross-system User Modeling and Personalization on the Social Web. UMUAI (to appear 2011)<br />B. Mehta. Cross System Personalization: Enabling personalization across multiple systems. PhD thesis, 2009.<br />
  44. 44. 2 Take-away Questions<br />Possible Future Work<br />
  45. 45. What kind of knowledge can we learn from users’ tagging and micro-blogging activities? <br />u1<br />t1<br />r1<br />t2<br />r2<br />u2<br />
  46. 46. Question<br />compose <br />answer<br />Answer<br />translate between query and Twitter vocabulary<br />How can we find “information” in social (micro-)streams?<br />see also TREC Microblogging Task:<br />
  47. 47. Thank you!<br />Twitter: @fabianabel<br /> <br />