Generating Resource Profiles by Exploitingthe Context of Social AnnotationsISWC, Bonn, Germany, Oct 27th 2011         Rica...
Social Annotations Folksonomies                                                                                          ...
Generating resource profiles      cool                                      Profile?                              Applicat...
Problems of traditional folksonomies                                 descriptive vs.                                 subje...
Exploiting Context in Folksonomies                                    music                                           Jazz...
Context in Social Tagging Systems• TagMe! – Tagging and  exploration front-end for  Flickrpictures that allows to attach  ...
context                       cool                                         Profile?                                       ...
Standard Resource Profiling            tag X                                                                 Resource  u1 ...
Context Profiling             tag A  u1                        R1                                                         ...
Generating context-based resource profiles     • Generic strategy for generating context-based resource       profiles:Con...
context                       cool                                         Profile?                                       ...
Context-based Weighting Strategies (1) 1. User-based co-occurrence:   •    Hypothesis: users tend to annotate similar reso...
Context-based Weighting Strategies (2)     2. Category-based co-occurrence:          •   Hypothesis: resources that occur ...
Context-based Weighting Strategies (3)     3. Semantic Meaning – URI-based co-occurrence:          •   Hypothesis: tags th...
Context-based Weighting Strategies (4)     4. Semantic Meaning – “binary”:       •   Hypothesis: tags that can be mapped t...
Context-based Weighting Strategies (5)5. Weighting based on Spatial context – area size:     •   Hypothesis: the larger th...
Context-based Weighting Strategies (6)6. Weighting based on Spatial context – distance   from center:     •    Hypothesis:...
Context-based Weighting Strategies (7)     7. Journal-based co-occurrence:          •    Hypothesis: tags that are assigne...
Context-based Weighting Strategies (8)     8. Journal-Year-based co-occurrence:       •   Hypothesis: tags that are assign...
Overview on Weighting Strategies                                                                  [TagMe!]                ...
Broken Slide      Generating Resource Profiles by Exploitingthe Context of Social Annotations   21
context                        cool                                         Profile?                                      ...
Experimental setup• “Tag Prediction” task (leave-one-out cross validation):     • remove one tag from the resource     • c...
Results [TagMe!]Context-based profiling strategies outperform                                 Semanticbaseline (tag freque...
Combining different types of context-   based profiling strategies                                                        ...
Results [BibSonomy]Again: Context-based profiling strategiesoutperform baseline (tag frequency) significantly.            ...
Conclusions• What we did: framework for generating resource profiles  by exploiting contextual information of social annot...
context                                                                Context-based                    cool              ...
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Generating Resource Profiles by Exploiting the Context of Social Annotations

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Slides presented at ISWC 2011 in Bonn, Germany. Corresponding paper: http://iswc2011.semanticweb.org/fileadmin/iswc/Papers/Research_Paper/12/70310288.pdf

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Generating Resource Profiles by Exploiting the Context of Social Annotations

  1. 1. Generating Resource Profiles by Exploitingthe Context of Social AnnotationsISWC, Bonn, Germany, Oct 27th 2011 Ricardo Kawase1, George Papadakis2, Fabian Abel31L3S Research Center, Leibniz University Hannover, Germany 2 ICCS, National Technical University of Athens, Greece 3Web Information Systems, TU Delft Delft University of Technology
  2. 2. Social Annotations Folksonomies armstrong baker,cool Users Tags armstrong, baker, dizzy, cool, dizzy, jazz armstrong jazzmusic, jazz, trumpet Resources• Folksonomy: • set of tag assignments • Formal model [Hotho et al. „07]: tag F = (U, T, R, Y) user resource tag assignment Generating Resource Profiles by Exploitingthe Context of Social Annotations 2
  3. 3. Generating resource profiles cool Profile? Applications concept weight weight that operate on ? resource profiles baker 1 (e.g. search, cool 2 content-based recommender)baker, cool • Resource profile = representation of a resource = set of weighted concepts • Straightforward approach = occurrence frequency of tags: SELECT tag, count(distinct user) FROM tas WHERE resource = XY GROUP BY tag Applications rely on good resource profiles! Generating Resource Profiles by Exploitingthe Context of Social Annotations 3
  4. 4. Problems of traditional folksonomies descriptive vs. subjective tags no tags armstrong baker, cool Tags armstrong, baker, dizzy, cool, dizzy, jazz armstrong jazzmusic, jazz, trumpet ambiguity synonyms of tags Generating valuable resource profiles becomes difficult Generating Resource Profiles by Exploitingthe Context of Social Annotations 4
  5. 5. Exploiting Context in Folksonomies music Jazz (noun) is a jazz style of music that… Resource Y created: 1979-12-06 context context context creator: … User X Age: 30 years Education: … context tag user resource jazz tag assignment User X• Context-enabled folksonomy: TAS XY created: 2011-04-19Fc = (U, T, R, Y,C, Z) meaning: dbpedia:Jazz - Cis the actual metadata information - Z Y xCis the set of context assignments Generating Resource Profiles by Exploitingthe Context of Social Annotations 5
  6. 6. Context in Social Tagging Systems• TagMe! – Tagging and exploration front-end for Flickrpictures that allows to attach three types of contexts to tag assignments: • Spatial information (assign tags to certain areas) • Categories (= tagging of tag assignments, e.g. buildings) • DBpediaURIs(meaning of tag assignments, dbpedia:Opera)• BibSonomy – Social resource sharing system for bookmarks and publications • Context of resources = BibTeX information of publications Generating Resource Profiles by Exploitingthe Context of Social Annotations 6
  7. 7. context cool Profile? concept weight ? context baker, coolHow can we exploit the contextof social annotations togenerate resource profiles? Generating Resource Profiles by Exploitingthe Context of Social Annotations 7
  8. 8. Standard Resource Profiling tag X Resource u1 Profile R1 R1 concept weight tag X 0.67 tag Y tag Y 0.33 tag X u2 tag-based resource profile tag assignments performed on resource R1• Exploiting the tags that have been directly assigned to the resource Generating Resource Profiles by Exploitingthe Context of Social Annotations 8
  9. 9. Context Profiling tag A u1 R1 Context Profile C1 context tag A C1 concept weight u2 R2 tag A 0.75 tag B0.25 tag A u3 R3 tag-based context tag B profile u3 R3 tag assignments in context folksonomy that refer to context C1• Aggregation of (context) profiles is possible ( again by means of mixture approach) Generating Resource Profiles by Exploitingthe Context of Social Annotations 9
  10. 10. Generating context-based resource profiles • Generic strategy for generating context-based resource profiles:Context-based Resource Context =α + (1-α) Resource Profile Profile Profileconcept weight concept weight concept weighttx 0.7 tx 0.85 tx 0.55ty 0.3 ty 0.15 ty 0.45 context Generating Resource Profiles by Exploitingthe Context of Social Annotations 10
  11. 11. context cool Profile? concept weight ? context baker, coolWeighting Strategies Generating Resource Profiles by Exploitingthe Context of Social Annotations 11
  12. 12. Context-based Weighting Strategies (1) 1. User-based co-occurrence: • Hypothesis: users tend to annotate similar resources  tags a user assigns to other resources are also relevant for the resource profile that should be constructed jazz Context-based u1 Resource Profile R1trumpet R1 concept weight jazz 0.67 trumpet 0.33 R2 Generating Resource Profiles by Exploitingthe Context of Social Annotations 12
  13. 13. Context-based Weighting Strategies (2) 2. Category-based co-occurrence: • Hypothesis: resources that occur in tag assignments that are classified in the same category are similar  “tags of that category” are also relevant for the resource jazz Category: Context-basedu1 R1 music Resource Profile R1 concept weight jazz 0.67 trumpet 0.33 trumpet u3 R3 Generating Resource Profiles by Exploitingthe Context of Social Annotations 13
  14. 14. Context-based Weighting Strategies (3) 3. Semantic Meaning – URI-based co-occurrence: • Hypothesis: tags that have the same meaning complement the tag-based resource profile positively jazz Context-based dbpedia:Jazz Resourceu1 R1 Profile R1 concept weight jazz 0.67 jazzmusic 0.33 jazzmusic u3 R3 Generating Resource Profiles by Exploitingthe Context of Social Annotations 14
  15. 15. Context-based Weighting Strategies (4) 4. Semantic Meaning – “binary”: • Hypothesis: tags that can be mapped to a DBpedia resource are more important than other tags jazz dbpedia:Jazz Context-basedu1 R1 Resource Profile R1 concept weight jazz 1 cb1981 0 cb1981 ?u3 R1 Generating Resource Profiles by Exploitingthe Context of Social Annotations 15
  16. 16. Context-based Weighting Strategies (5)5. Weighting based on Spatial context – area size: • Hypothesis: the larger the area to which a tag is assigned to the more important the tag for the resource Context-based chet baker Resource Profile R1 R1 concept weight jazz 0.67 trumpet 0.33u1 trumpet R1 Generating Resource Profiles by Exploitingthe Context of Social Annotations 16
  17. 17. Context-based Weighting Strategies (6)6. Weighting based on Spatial context – distance from center: • Hypothesis: the closer the (centroid of the) area to the center of the picture the more important the tag for the resource Context-based tag1 Resource Profile R5 concept weight tag1 0.83 tag2 0.17 R5u1 tag2 distance(tag1) <distance(tag2) Generating Resource Profiles by Exploitingthe Context of Social Annotations 17
  18. 18. Context-based Weighting Strategies (7) 7. Journal-based co-occurrence: • Hypothesis: tags that are assigned to publications that were published in the same journal are also relevant for the resource Context-based Resource SPARQL Journal: Web Profile R1u1 R1 Semantics concept weight SPARQL 0.67 semantics 0.33 semantics u3 R3 R1 Generating Resource Profiles by Exploitingthe Context of Social Annotations 18
  19. 19. Context-based Weighting Strategies (8) 8. Journal-Year-based co-occurrence: • Hypothesis: tags that are assigned to publications that were published in the same journal AND in the same year are also relevant for the resource Context-based year: 2007 Resource SPARQLu1 R1 Profile R1 concept weight SPARQL 0.67 Journal: Web RDF store 0.33 RDF store Semanticsu3 R3 R1 trust year: 2009u5 R16 Generating Resource Profiles by Exploitingthe Context of Social Annotations 19
  20. 20. Overview on Weighting Strategies [TagMe!] Based on: Baseline: - Categories Tag-based co-occurrence - Spatial information frequency - Semantic meaningUser-based context tag user resource tag assignment Resource-based: - BibTeX properties [BibSonomy] Combining strategies more than 120 context-based profiling strategies for TagMe! Generating Resource Profiles by Exploitingthe Context of Social Annotations 20
  21. 21. Broken Slide Generating Resource Profiles by Exploitingthe Context of Social Annotations 21
  22. 22. context cool Profile? concept weight ? context baker, coolWhich resource profiling strategygenerates the most valuableprofiles? Generating Resource Profiles by Exploitingthe Context of Social Annotations 22
  23. 23. Experimental setup• “Tag Prediction” task (leave-one-out cross validation): • remove one tag from the resource • create (context-based) resource profile • use profile to create a ranking of tags  hidden tag should be at the top of the ranking• Baseline:tag co-occurrence• Metrics: Success@k = probability that the relevant tag appear within the top k of the ranking• Data sets: TagMe!Tag Assignments (TAs) 1,288TAs with Spatial Information 671 BibSonomyTAs with Category Information 917 Resources 566,939TAs with URI Information 1,050 Users 6,569 Tag Assignments (TAs) 2,622,423TAs with all information 432 Generating Resource Profiles by Exploitingthe Context of Social Annotations 23
  24. 24. Results [TagMe!]Context-based profiling strategies outperform Semanticbaseline (tag frequency) significantly. meaning and spatial information allow for best performance. Area size more valuable than distance to center no significant difference w.r.t. category- and user- based strategy Generating Resource Profiles by Exploitingthe Context of Social Annotations 24
  25. 25. Combining different types of context- based profiling strategies Mixture of context-based strategies improve performance (by 37%) Context-based strategies have to be combined intelligently in order to increase cumulative gain in performance. Generating Resource Profiles by Exploitingthe Context of Social Annotations 25
  26. 26. Results [BibSonomy]Again: Context-based profiling strategiesoutperform baseline (tag frequency) significantly. The more specific the context, the better the performance ( reducing noise) Generating Resource Profiles by Exploitingthe Context of Social Annotations 26
  27. 27. Conclusions• What we did: framework for generating resource profiles by exploiting contextual information of social annotations • Context-based folksonomy model • Set of context-based resource profiling strategies (both generic and application-specific strategies) • Evaluation in two social tagging systems: TagMe! and BibSonomy• Results: • Context-based strategies outperform other strategies that do not exploit contextual information • Context of tag assignments (e.g. semantic meaning) allows for best performance • Context of the user who performs the tag assignment is competitive • Mixing context-based strategies improves quality but does not necessarily result in a cumulative gain in performance (“over- contextualization”)  smart mixing performs best (>40% improvement) Generating Resource Profiles by Exploitingthe Context of Social Annotations 27
  28. 28. context Context-based cool Resource Profile concept weightcontext SPARQL 0.67 semantics 0.33 baker, cool Twitter: Thank you! @ricardokawase @gpapadis @fabianabel Generating Resource Profiles by Exploitingthe Context of Social Annotations 28
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