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

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

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

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

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

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

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

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Geo-information communities Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Geo-information communities n. 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) Schlieder: Modelling Collaborative Semantics 08 -8

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

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Conceptual modelling Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Conceptual modelling n. High quality } 30 experts from cadastral agencies, GIScience and Knowledge Engineering } Description logic-based modelling (OWL-DL) n. High cost } 4 years for understanding and modelling property transaction processes Schlieder: Modelling Collaborative Semantics COST G 9 Modelling real property transactions 08 -10

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

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

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

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

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

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

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Tag frequency Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Tag frequency n. Example } 422. 895 images } 2. 784 categories (tags) n. Power law } frequency rank - } 36% tags used once only 24% tags used 2 -5 times } Most frequent tag used 17. 360 times rank tag 1 Church 2 Farmland 3 Farm … 2782 Windmill stump 2783 Luminous object in space (sun) 2784 Penstock www. geograph. org. uk Schlieder: Modelling Collaborative Semantics 08 -17

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

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

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

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Multi-object tagging Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Multi-object tagging Semantic analysis tagging({obj 1, …, obj. N}, tag, user) place name tag Collection of objects Schlieder: Modelling Collaborative Semantics 08 -21

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

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Bug or Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Bug or feature? n. Data source (feature) } Multi-object user tagging informs us about different conceptualizations Schlieder: Modelling Collaborative Semantics n. 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 08 -23

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing The semantics Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing The semantics of multi-object tags n. 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 Schlieder: Modelling Collaborative Semantics 08 -24

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

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Conceptual modelling Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Conceptual modelling n Issues } How can we describe the semantics of place concepts? } How do we obtain data about different conceptualizations? n Selection task } Selection seems based on two conflictin criteria: typicality and variability n Multi-object tagging } User tagging of multi-objects informs about the place concepts of individual users } tagging({obj 1, …, obj. N}, tag, user) Schlieder: Modelling Collaborative Semantics 08 -26

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

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

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

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

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Feature similarity Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Feature similarity Anna Bamberg Bill Clio Don 2 -4 -5 1 -4 -6 2 -3 -5 1 -4 -6 2 -4 -6 1 -4 -5 1 -2 -3 1 -3 -6 4 -5 -6 1 -2 -3 2 -5 -6 1 -3 -6 Cardiff Southern Germany 1 -2 -6 2 -4 -6 3 -5 -6 Southeast of England 2 -4 -5 2 -5 -6 1 -3 -5 Emma Franz 2 -3 -4 sim(A, B) = |A∩B| / |A∪B| 2/3 0. 66 Schlieder: Modelling Collaborative Semantics 08 -31

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing User-to-user similarity Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing User-to-user similarity Anna Clio Don Emma Franz . 66 Anna Bill . 22 . 66 . 33 . 66 . 22 . 33 . 44 . 77 . 55 . 66 . 42 . 33 . 55 Bill . 66 Clio . 22 Don . 66 . 33 . 55 Emma . 33 . 44 . 66 . 33 Franz . 66 . 77 . 42 . 55 Schlieder: Modelling Collaborative Semantics . 33 08 -32

Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Spatial similarity Chair of Computing in the Cultural Sciences Laboratory for Semantic Information Processing Spatial similarity n. 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 Schlieder: Modelling Collaborative Semantics 08 -33

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

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

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

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