Knowledge Management Institute




              Extracting Semantics from Crowds
                     Stanford Center for...
Knowledge Management Institute




            with …
            D. H li C Kö
            D Helic, C. Körner, J. Pöschko,...
Knowledge Management Institute



                            Classification Systems
                     in Information a...
Knowledge Management Institute



                                 Crowdsourcing Semantics:
                              ...
Knowledge Management Institute




                                 Research Agenda
       Vision:
       Vi i
         Ut...
Knowledge Management Institute




     Extracting Semantics from …

     Motivation

     Social Labeling
     • Hashtag ...
Knowledge Management Institute



                                 Activities of Users Online
     Users engage in…

     ...
Knowledge Management Institute




     Extracting Semantics from Crowds

     Motivation

     Social Labeling
     • Has...
Knowledge Management Institute



                                        Social Labeling
                                ...
Markus Strohmaier
                                                                                                        ...
Markus Strohmaier
                                                                                                        ...
Knowledge Management Institute




     Extracting Semantics from Crowds

     Motivation

     Social Labeling
     • Has...
Knowledge Management Institute



                                       Social Tagging
                                  ...
Knowledge Management Institute




                                                        Tag Relatedness




           ...
Knowledge Management Institute




                                                            Tag Generality

           ...
Knowledge Management Institute




                                                               Tag Hierarchy


        ...
Knowledge Management Institute




     Extracting Semantics from Crowds

     Motivation

     Social Labeling
     • Has...
Knowledge Management Institute




                                 Social Navigation
                                    ...
Knowledge Management Institute




                                 Prototype: HANNE
            HANNE: Holistic Applicati...
Knowledge Management Institute




                   Navigational Knowledge Engineering
                                 ...
Knowledge Management Institute




                   Navigational Knowledge Engineering




                             ...
Knowledge Management Institute




                                  Inductive Concept Learning




            To test th...
Knowledge Management Institute



                                 HANNE &
                 The Problem of Inductive Conce...
Knowledge Management Institute




                                                                                       ...
Knowledge Management Institute




                                                  Concepts Learned




         • The L...
Knowledge Management Institute




                                                                      Result
          ...
Knowledge Management Institute
                                 http://aksw.org/Projects/NKE


                           ...
Knowledge Management Institute




                                  Summary
            We
            W can observe that...
Knowledge Management Institute




                                                  Outlook
            Current and plann...
Knowledge Management Institute




 A N What‘s the fknowledge of SAS? h
   New A s d for S
     What
       Agenda      Se...
Knowledge Management Institute




                                        Thank You.



                                 ...
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Extracting Semantics from Crowds

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Stanford Center for Biomedical Informatics Research, Colloquium, Stanford University, Palo Alto, CA , Jan 6th, 2010

Presenter: Markus Strohmaier, Graz University of Technology, Austria

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Extracting Semantics from Crowds

  1. 1. Knowledge Management Institute Extracting Semantics from Crowds Stanford Center for Biomedical Informatics Research, Colloquium Stanford University, Palo Alto, CA Jan 6th, 2010 Markus Strohmaier Assistant Professor Graz University of Technology, Austria Visiting Scientist (XEROX) PARC, USA Markus Strohmaier 2011 1
  2. 2. Knowledge Management Institute with … D. H li C Kö D Helic, C. Körner, J. Pöschko, C. W J Pö hk C Wagner (Graz University of Technology, Austria) D. Benz, G D Benz G. Stumme (U. of Kassel, Germany) A. Hotho (U. of Würzburg, Germany) S. Hellmann, J. Lehmann, C. Stadler, J. Unbehauen (U. Leipzig, (U of Leipzig Germany) Markus Strohmaier 2011 2
  3. 3. Knowledge Management Institute Classification Systems in Information and Library Sciences Usually produced and maintained by few (e.g. (e g dozens of) domain experts experts. but: used by many (potentially millions). Can a very large group (a crowd) of users contribute to ontology engineering efforts? Markus Strohmaier 2011 3
  4. 4. Knowledge Management Institute Crowdsourcing Semantics: An Experiment (2008) LoC L C posted 3000 photos t d h t to flickr: 24 hours after launch: • over 4,000 unique tags • about 19,000 tags added Output? noisy, How can this data contribute to the unstructured, unqualified, induction of semantic structures? weak semantics Markus Strohmaier 2011 4
  5. 5. Knowledge Management Institute Research Agenda Vision: Vi i Utilizing online behavior of crowds for the construction, maintenance and enrichment construction of large-scale semantic structures. Mission: • to model behavior of large numbers (millions) of users online • to develop techniques and algorithms that acquire semantic structures from users‘ interaction with data • to influence user behavior and emerging semantics • to evaluate results Markus Strohmaier 2011 5
  6. 6. Knowledge Management Institute Extracting Semantics from … Motivation Social Labeling • Hashtag Semantics Social Tagging • Tag Relatedness • Tag Generality • Tag Hierarchies Social Navigation • Navigational Knowledge Engineering g g Markus Strohmaier 2011 6
  7. 7. Knowledge Management Institute Activities of Users Online Users engage in… Labeling Tagging Navigation Can we tap into the outcome of these activities to extract semantic structures? Markus Strohmaier 2011 7
  8. 8. Knowledge Management Institute Extracting Semantics from Crowds Motivation Social Labeling • Hashtag Semantics Social Tagging • Tag Relatedness • Tag Generality • Tag Hierarchies Social Navigation • Navigational Knowledge Engineering g g Markus Strohmaier 2011 8
  9. 9. Knowledge Management Institute Social Labeling Example: Twitter users label short messages with concepts (hashtags) Which hashtags behave as strong identifiers (if any), and could they be mapped to concept identifiers in the Semantic Web (URIs)? [Laniado and Mika 2010] Markus Strohmaier 2011 9
  10. 10. Markus Strohmaier Knowledge Management Institute with and C.Wagner 2011 J. Pöschko ConceptNet developed by C. Wagner, M. Strohmaie The Wisdom in Tw W er, weetonomies: Acquiri ing Latent Conceptua Structures from So al ocial Awareness Stre eams, 10 Semmantic Search 2010 Workshop (SemSearch2 W 2010), in conjunction with the 19th Internatio w onal World Wide Web Conference (WWW20 C 010), Rale eigh, NC, USA, April 26-30, ACM, 2010. 2
  11. 11. Markus Strohmaier Knowledge Management Institute such a network? p with X is a subconcept of Y e.g. X is more general than Y, and C.Wagner 2011 J. Pöschko ConceptNet Can we qualify semantic associations in ent Ev- ent Ev- developed by C. Wagner, M. Strohmaie The Wisdom in Tw W er, weetonomies: Acquiri ing Latent Conceptua Structures from So al ocial Awareness Stre eams, 11 Semmantic Search 2010 Workshop (SemSearch2 W 2010), in conjunction with the 19th Internatio w onal World Wide Web Conference (WWW20 C 010), Rale eigh, NC, USA, April 26-30, ACM, 2010. 2
  12. 12. Knowledge Management Institute Extracting Semantics from Crowds Motivation Social Labeling • Hashtag Semantics Social Tagging • Tag Relatedness • Tag Generality • Tag Hierarchies Social Navigation • Navigational Knowledge Engineering g g Markus Strohmaier 2011 12
  13. 13. Knowledge Management Institute Social Tagging Example: Delicious Users label and categorize Resources resources with concepts (tags) Tags Users U A folksonomy is a tuple F:= (U, T, R, Y) where • th th the three di j i t fi it sets U T R correspond t disjoint, finite t U, T, d to user 1 – a set of persons or users u ∈ U – a set of tags t ∈ T and – a set of resources or objects r ∈ R tag 1 res. 1 • Y ⊆ U ×T ×R, called set of tag assignments Markus Strohmaier 2011 13
  14. 14. Knowledge Management Institute Tag Relatedness Different tag similiarity measures applied to del.icio.us C. Cattuto, D. Benz, A. Hotho, G. Stumme, Semantic Grounding of Tag Relatedness in Social Bookmarking Systems, 7th International Semantic Web Conference ISWC2008, LNCS 5318, 615-631 (2008). Markus Strohmaier 2011 14
  15. 15. Knowledge Management Institute Tag Generality frequency Del.icio.us with D. Benz et al. D. Benz, C. Körner, A. Hotho, G. Stumme, M. Strohmaier, One Tag to bind them all: Measuring Term Abstractness in Social Metadata, Submitted to ESWC 2011. Markus Strohmaier 2011 15
  16. 16. Knowledge Management Institute Tag Hierarchy TP..Taxonomic TR..Taxonomic TF..Taxonomic TO…Taxonomic precision recall F measure overlap Different folksonomy algorithms with D. Helic et al. M. Strohmaier, D. Helic, D. Benz. Evaluation of Folksonomy Induction Algorithms. Submitted to the ACM Transactions on Intelligent Systems and Technology, (2011). Markus Strohmaier 2011 16
  17. 17. Knowledge Management Institute Extracting Semantics from Crowds Motivation Social Labeling • Hashtag Semantics Social Tagging • Tag Relatedness • Tag Generality • Tag Hierarchies Social Navigation • Navigational Knowledge Engineering g g Markus Strohmaier 2011 17
  18. 18. Knowledge Management Institute Social Navigation Users search for and navigate to certain sets of resources Can we turn ordinary users into contributors for ontology enrichment? t l i h t? Navigational Knowledge Engineering: A light-weight methodology for low-cost knowledge engineering by a massive user base. Markus Strohmaier 2011 18
  19. 19. Knowledge Management Institute Prototype: HANNE HANNE: Holistic Application for Navigational HANNE H li ti A li ti f N i ti l Knowledge Engineering http://aksw.org/Projects/NKE http://aksw org/Projects/NKE with Sebastian Hellmann et al., University of Leipzig Markus Strohmaier 2011 19
  20. 20. Knowledge Management Institute Navigational Knowledge Engineering http://aksw.org/Projects/NKE Example: Extending DBPedia with NKE Markus Strohmaier 2011 S. Hellmann, J. Lehmann, C. Stadler, J. Unbehauen, M. Strohmaier, Navigational Knowledge Engineering, Submitted to WWW 2011. 20
  21. 21. Knowledge Management Institute Navigational Knowledge Engineering Choose initial positive and negative examples from the search result. Here we are looking for Football Clubs in Saxony, a region in German Germany. Markus Strohmaier 2011 http://aksw.org/Projects/NKE 21
  22. 22. Knowledge Management Institute Inductive Concept Learning To test the coverage, the function Returns the value true if e is covered by H, and false otherwise. Markus Strohmaier 2011 Nada Lavrac and Saso Dzeroski, Inductive Logic Programming: Techniques and Applications, 1994 22
  23. 23. Knowledge Management Institute HANNE & The Problem of Inductive Concept Learning given: • Background knowledge (OWL/DL knowledge base) • positive and negative examples of a concept (instances) (i t ) find: fi d • A hypothesis (expressed as OWL class descriptions) that covers all positive and no negative examples Markus Strohmaier 2011 23
  24. 24. Knowledge Management Institute Based on the extension, ICL searches for suitable hypotheses. Markus Strohmaier 2011 S. Hellmann, J. Lehmann, C. Stadler, J. Unbehauen, M. Strohmaier, Navigational Knowledge Engineering, Submitted to WWW 2011. 24
  25. 25. Knowledge Management Institute Concepts Learned • The Learned C Concept is shown in Manchester O OWL SSyntax • The user can retain the concept for later retrieval. • Saved concepts are displayed as social navigation suggestions. Can be used to enrich existing knowledge base. base Markus Strohmaier 2011 S. Hellmann, J. Lehmann, C. Stadler, J. Unbehauen, M. Strohmaier, Navigational Knowledge Engineering, Submitted to WWW 2011. 25
  26. 26. Knowledge Management Institute Result Useful properties: • Biased towards high recall • Scales well: Number of training examples is more important than the size of the background knowledge With only 2 positives and 4 negatives, it is possible to find 13 more instances, which are f tb ll clubs i t hi h football l b situated close to Saxony, Germany Markus Strohmaier 2011 S. Hellmann, J. Lehmann, C. Stadler, J. Unbehauen, M. Strohmaier, Navigational Knowledge Engineering, Submitted to WWW 2011. 26
  27. 27. Knowledge Management Institute http://aksw.org/Projects/NKE Mock up (1) Markus Strohmaier 2011 27
  28. 28. Knowledge Management Institute Summary We W can observe that: b th t • semantic structures can be obtained as a byproduct of online crowd behavior (t i categorizing, navigation) (tagging, t i i i ti ) • these structures can approximate structures in reference knowledge bases (DBPedia WordNet etc) (DBPedia, WordNet, but: • different levels of scale mean different degrees of formality and quality of semantic structures • pragmatics influences resulting semantics Markus Strohmaier 2011 28
  29. 29. Knowledge Management Institute Outlook Current and planned collaborations: C t d l d ll b ti (XEROX) PARC, USA and U. Würzburg, Germany: • PoSTS: Interactions between pragmatics and semantics in social tagging systems • DFG/FWF coop. project proposal 2011 2014 (~400.000 EUR, under review) 2011-2014 ( 400.000 • Visiting Scholar with (XEROX) PARC in 2010/2011 Media-X, Stanford U., USA: • CALHWIN: Implementation of a social health information network for California Semantic analysis of social media • NSF project proposal 2011-2014 (under review) U. Of Leipzig, Germany: • Navigational Knowledge Engineering Related events I am involved in: • ACM SIGWEB Working Group on Social Media (Co-chair) • ACM Hypertext 2011 track „Social Media“ (Co-chair) • WWW‘2011 Workshop on Usage Analysis and the Web of Data (PC member) W k h U A l i d th W b f D t b ) Markus Strohmaier 2011 29
  30. 30. Knowledge Management Institute A N What‘s the fknowledge of SAS? h New A s d for S What Agenda Semantic R ti Research 7 January, 2011 Web Resources Natural Language Constructs Real-World Happenings Utilizing online behavior of crowds for 30 the construction, maintenance and enrichment of large-scale semantic structures Markus Strohmaier 2011 http://www.flickr.com/photos/waldoj/722508166/ http://www.flickr.com/photos/matthewfield/2306001896/ 30
  31. 31. Knowledge Management Institute Thank You. Acknowledgements collaborators and co-authors: D. Helic, C. Körner, J. Pöschko, C. Wagner (Graz U. of Technology, Austria) D. Benz, G. Stumme (U. of Kassel, Germany) A. Hotho (U. of Würzburg, Germany) S. Hellmann, J. Lehmann, C. Stadler, J. Unbehauen (U. of Leipzig, Germany) Markus Strohmaier 2011 31

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