1. Modelling Collaborative Semantics with a Geographic Recommender Christoph Schlieder SeCoGIS Workshop November 7, 2007, Auckland Lehrstuhl für Angewandte Informatik in den Kultur-, Geschichts- und Geowissenschaften Otto-Friedrich-Universität Bamberg

2. 08-2 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics A geographic place Bamberg  UNESCO world heritage site

3. 08-3 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics A different conceptualization Bamberg  beer capital of Bavaria

4. 08-4 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Yet another conceptualization daily.elsch.eu Bamberg  ?

5. 08-5 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Conceptual modelling Place concepts  „Bamberg“, „Southern Germany“, „Europe“, …  Thematically and spatially different conceptualizations Issues  Formal semantics of place concepts  Data about different conceptualizations Contributions  Semantic analysis based on multi-object (!) tagging  User similarity data from a geographic recommender

6. 08-6 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Tripost Recommender

7. 08-7 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Part 1 Geo-information communities Part 2 Collaborative Semantics Part 3 Geographic Recommender

8. 08-8 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Geo-information communities Information community  Gould & Hecht (2001) A Framework for Geospatial and Statistical Information OGC white paper An information community is a group of people who share a common geospatial feature data dictionary (including definitions of feature relationships) and a common metadata schema. Gould & Hecht (2001)

9. 08-9 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Example Cadastral Communities  Data and process models  27 national cadastral authorities in the EU  1 community designing the Cadastral Reference Model Ontological engineering  One ontology per information community Cadastral Reference Model Lemmen et al. (2003)

10. 08-10 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Conceptual modelling High quality  30 experts from cadastral agencies, GIScience and Knowledge Engineering  Description logic-based modelling (OWL-DL) High cost  4 years for understanding and modelling property transaction processes COST G9 Modelling real property transactions

11. 08-11 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Ontological engineering Hess, Schlieder (2006) Ontology-based Verification of Core Model Conformity, CEUS National cadastral data model + intended correspondences Core Cadastral Data Model + conformity constraints Conformity checker (OWL-DL)

12. 08-12 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Information communities Traditional view  Each information community defines its ontology  Number of communities or ontologies << 100  Complex conceptualization uses DL role restrictions Semantic boundaries  Ontologies come with crisp semantic boundaries  The Greek cadastral model is not the Danish model  Semantic Web technologies are appropriate (OWL-DL)

13. 08-13 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Part 1 Geo-information communities Part 2 Collaborative Semantics Part 3 Geographic Recommender

14. 08-14 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Collaborative geodata acquisition Social Web  Communities of users who collect geospatial data Collaborative mapping  GPS biking trail libraries Morris et al. (2004), Matyas (2007)  Public domain street maps www.openstreetmap.org dense data for London sparse data for Brussels www.openstreetmap.org

15. 08-15 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Collaborative metadata acquisition Social tagging  Categorization of geospatial data by a community  Keywords („tags“) describe spatio-temporal coverage and content type Folksonomies  folk taxonomy = tag vocabulary www.geograph.org.uk

16. 08-16 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Tagging as categorization task tagged by data producer farm track

17. 08-17 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Tag frequency Example  422.895 images  2.784 categories (tags) Power law  frequency  rank -   36% tags used once only 24% tags used 2-5 times  Most frequent tag used 17.360 times ranktag 1Church 2Farmland 3Farm … 2782Windmill stump 2783Luminous object in space (sun) 2784Penstock www.geograph.org.uk

18. 08-18 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Spatial coverage www.panoramio.com/photo/201427 Neuschwanstein POI in Google maps

19. 08-19 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Folksonomies Low cost  Categorization by voluntary contributors (non-experts) Low quality  No controlled vocabulary house vs. house manson vs. manor house  misclassifications Misclassification by a non-expert

20. 08-20 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics The semantics of tags User tagging  Not just the contributor but all users provide tags  Conflicting tags (!) Semantic analysis  Ternary semantic relation for user tagged data Classical view tagging(object, tag) Gruber (2005) tagging(object, tag, user)

21. 08-21 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Multi-object tagging Collection of objects place name tag Semantic analysis tagging({obj1,…,objN}, tag, user)

22. 08-22 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Bug or feature? Quality problem (bug)  Serious for folksonomies  Even more serious if user tagging is permitted  Unmanageable for multi- object user tagging? Consequence  Use folksonomies only as the poor man‘s ontology

23. 08-23 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Bug or feature? Data source (feature)  Multi-object user tagging informs us about different conceptualizations Consequence  Invert the task of finding a tag for a multi-object  Find n objects from a collection of m >> n to illustrate a (place) concept

24. 08-24 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics The semantics of multi-object tags Hypothesis  Selection is based on two conflicting criteria  Typicality: choose typical instances of the concept  Variablity: show the variability of the concept violation of the variability criterion

25. 08-25 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Empirical data

26. 08-26 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Conceptual modelling Issues  How can we describe the semantics of place concepts?  How do we obtain data about different conceptualizations? Selection task  Selection seems based on two conflictin criteria: typicality and variability Multi-object tagging  User tagging of multi-objects informs about the place concepts of individual users  tagging({obj1,…,objN}, tag, user)

27. 08-27 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Part 1 Geo-information communities Part 2 Collaborative Semantics Part 3 Geographic Recommender

28. 08-28 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Recommender systems Item-to-item similarity recommendations www.amazon.com

29. 08-29 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Multi-object recommendation Use case  The user selects images and captions for a patchwork postcard.  The system generates other patchwork postcards with appropriate captions  www.wiai.uni- bamberg.de/tripost TriPost Webservice

30. 08-30 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Tags of a single user Bamberg Cardiff Dublin Antwerpen Anna‘s multi-object tags

31. 08-31 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Feature similarity AnnaBillClioDonEmmaFranz Bamberg1-3-62-4-51-4-62-3-4 Cardiff2-3-51-4-62-4-61-4-51-2-3 Southern Germany 1-2-62-4-63-5-61-3-64-5-61-2-6 Southeast of England 2-4-52-5-61-3-51-2-32-5-6 sim(A,B) = |A∩B| / |A ∪ B| 2/3  0.66

32. 08-32 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics User-to-user similarity AnnaBillClioDonEmmaFranz Anna.66.22.66.33.66 Bill.66.22.33.44.77 Clio.22.55.66.42 Don.66.33.55.33.55 Emma.33.44.66.33 Franz.66.77.42.55.33

33. 08-33 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Spatial similarity Spatial Partonomy  Users visiting a similar selection of places are considered similar  Example: Europe in 7 days Which Countries? Which Cities? Which Fotographs? Printed patchwork postcard

34. 08-34 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Computing similarity Measures  Feature similarity e.g. Tversky measure  User-user similarity e.g. averaged feature similarity Central idea  User-to-user similarity in the selection task is interpreted as a measure for shared conceptualization Information community  The community of a user u consists of the k users most similar to u.

35. 08-35 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Tag Communities A D F B C E C A B A D F B C E E FD F  3-neighbors(C)C  3-neighbors(F) Fuzzy semantic boundary 2-, 3-, 4-community?

36. 08-36 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Conclusions Issues  Formal semantics of place concepts  Data about different conceptualizations Contributions  Semantic analysis based on multi-object (!) tagging  User similarity data from a geographic recommender Consequences  Tagging communities are different from information communities

37. 08-37 Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Schlieder: Modelling Collaborative Semantics Conclusions Folksonomies  modeling of semantics before the emergence of information communities  before crisp semantic boundaries have been established Semantic Web ontologies  modeling of semantics after that phase  they assume crisp semantic boundaries