Modelling Collaborative Semantics with a Geographic Recommender Christoph Schlieder

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

2. Bamberg UNESCO world heritage site A geographic place Germany Schlieder: Modelling Collaborative Semantics

3. Bamberg beer capital of Bavaria A different conceptualization Germany Schlieder: Modelling Collaborative Semantics

4. Yet another conceptualization daily.elsch.eu Bamberg ? Germany Schlieder: Modelling Collaborative Semantics

5. 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 Conceptual modelling Schlieder: Modelling Collaborative Semantics

6. Tripost Recommender Schlieder: Modelling Collaborative Semantics

7. Part 1Geo-information communities Part 2Collaborative Semantics Part 3 Geographic Recommender Schlieder: Modelling Collaborative Semantics

8. Information community Gould & Hecht (2001)A Framework for Geospatial and Statistical InformationOGC 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) Geo-information communities Schlieder: Modelling Collaborative Semantics

9. 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 Example Cadastral Reference Model Lemmen et al. (2003) Schlieder: Modelling Collaborative Semantics

10. 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 Conceptual modelling COST G9 Modelling real property transactions Schlieder: Modelling Collaborative Semantics

11. Core Cadastral Data Model + conformity constraints Conformity checker (OWL-DL) National cadastral data model + intended correspondences Ontological engineering Hess, Schlieder (2006) Ontology-based Verification of Core Model Conformity, CEUS Schlieder: Modelling Collaborative Semantics

12. 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) Information communities Schlieder: Modelling Collaborative Semantics

13. Part 1Geo-information communities Part 2Collaborative Semantics Part 3 Geographic Recommender Schlieder: Modelling Collaborative Semantics

14. Social Web Communities of users who collect geospatial data Collaborative mapping GPS biking trail librariesMorris et al. (2004), Matyas (2007) Public domain street mapswww.openstreetmap.org Collaborative geodata acquisition www.openstreetmap.org dense data for London www.openstreetmap.org sparse data for Brussels Schlieder: Modelling Collaborative Semantics

15. Social tagging Categorization of geospatial data by a community Keywords („tags“) describe spatio-temporal coverage and content type Folksonomies folk taxonomy= tag vocabulary Collaborative metadata acquisition www.geograph.org.uk Schlieder: Modelling Collaborative Semantics

16. tagged by data producer farm track Tagging as categorization task Schlieder: Modelling Collaborative Semantics

17. Example 422.895 images 2.784 categories (tags) Power law frequency  rank - 36% tags used once only24% tags used 2-5 times Most frequent tag used 17.360 times Tag frequency www.geograph.org.uk Schlieder: Modelling Collaborative Semantics

18. www.panoramio.com/photo/201427 Spatial coverage Neuschwanstein POI in Google maps Schlieder: Modelling Collaborative Semantics

19. Low cost Categorization by voluntary contributors (non-experts) Low quality No controlled vocabularyhouse vs. housemanson vs. manor house misclassifications Folksonomies Misclassification by a non-expert Schlieder: Modelling Collaborative Semantics

20. User tagging Not just the contributor but all users provide tags Conflicting tags (!) Semantic analysis Ternary semantic relation for user tagged data The semantics of tags Classical view tagging(object, tag) Gruber (2005) tagging(object, tag, user) Schlieder: Modelling Collaborative Semantics

21. Germany Germany Germany Multi-object tagging Semantic analysis tagging({obj1,…,objN}, tag, user) place name tag Collection of objects Schlieder: Modelling Collaborative Semantics

22. 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 Bug or feature? Schlieder: Modelling Collaborative Semantics

23. 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 Bug or feature? Schlieder: Modelling Collaborative Semantics

24. Hypothesis Selection is based on two conflicting criteria Typicality: choose typical instances of the concept Variablity: show the variability of the concept Germany The semantics of multi-object tags violation of the variability criterion Schlieder: Modelling Collaborative Semantics

25. Empirical data Schlieder: Modelling Collaborative Semantics

26. 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) Conceptual modelling Schlieder: Modelling Collaborative Semantics

27. Part 1Geo-information communities Part 2Collaborative Semantics Part 3 Geographic Recommender Schlieder: Modelling Collaborative Semantics

28. Recommender systems Item-to-item similarity recommendations www.amazon.com Schlieder: Modelling Collaborative Semantics

29. 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 Multi-object recommendation TriPost Webservice Schlieder: Modelling Collaborative Semantics

30. Anna‘s multi-object tags Antwerpen Bamberg Cardiff Dublin Tags of a single user Schlieder: Modelling Collaborative Semantics

31. Feature similarity sim(A,B) = |A∩B| / |A∪B| 2/3  0.66 Schlieder: Modelling Collaborative Semantics

32. User-to-user similarity Schlieder: Modelling Collaborative Semantics

33. Spatial Partonomy Users visiting a similar selection of places are considered similar Example: Europe in 7 daysWhich Countries? Which Cities? Which Fotographs? Spatial similarity Printed patchwork postcard Schlieder: Modelling Collaborative Semantics

34. Measures Feature similaritye.g. Tversky measure User-user similaritye.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. Computing similarity Schlieder: Modelling Collaborative Semantics

35. A E B A A E E B B D F F D C C D F C Tag Communities Fuzzy semantic boundary 2-, 3-, 4-community? F  3-neighbors(C) C  3-neighbors(F) Schlieder: Modelling Collaborative Semantics

36. 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 Schlieder: Modelling Collaborative Semantics

37. 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 Schlieder: Modelling Collaborative Semantics