Putting Geographic Information Ontologies to Work The Case of Geospatial Science

1. GEOINFO 2013 – XIV Brazilian Symposium on Geoinformatics – November 2013 Putting Geographic Information Ontologies to WorkThe Case of Geospatial Science Helen Couclelis Geography Department University of California Santa Barbara California, USA

2. Is GIScience ‘working’ hard enough for us? • great theoretical work = great practical benefits? • Werner Kuhn • VGI, trust, and clean wells in Africa • Kathleen Stewart & Christophe Claramunt • Call for papers: • Spatio-temporal theories and models for environmental, urban and social sciences • Gilberto Câmara & team • geoinformatics and … and… and…

3. and Helen Couclelis? • Early enthusiasm • models!planning! • spatial interaction, CA, ABM * • Mature doubts • uncertainty beyond data • forecasts and policy • Mature enthusiasm • the ‘big picture’ • ontologyand representation in space and time *EU’s FuturICT shortlisted project

4. Why ontology? What Ontology?... • from Plato to SUMO and DOLCE • Worldversusmicro-worlds • at first • interoperability • then cognition, language, structure, meaning, concepts, measurements, physical /non-physical entities, space, time, user, culture, reality, philosophy • recently • micro-ontologies • microtheoriesand the Semantic Web

5. http://keet.wordpress.com/category/philosophy/ontology/

7. Note Gruber’s agent-centered definition: • An ontology is “a formal, explicit specification of a sharedconceptualization” • “… an ontology is a description … of the concepts and relationships that can exist for an agent or a community of agents.” • Ontologies must “constrain the possible interpretationsfor the defined terms.”

8. ontologies are social artifacts • “The ultimate source of meaning is the physical world and the agents who use signs to represent entities in the world and their intentionsconcerning them”. • (Sowa)

9. Overview • (Introduction) • Representation and the Big Picture in GISc • Ontologies of Geographic Information • A micro-ontology generating engine? • Geodesign: an application • Questions & Discussion

10. This is not a pipe The map is not the territory The model is not reality Representation and the Big Picture

11. GIScience and the big-picturequestions • frameworks, general theories, ontologies, base models • “The challenge of representing fields-objects in a computer environment” (Camara 2000) • “Field-object integration through a common base model” (Kjenstad 2006) • “A general theory to bring many previous ideas under a single umbrella” (Goodchild et al. 2007) • “Need for a conceptually unifying data model” (Voudouris 2010)

12. Camara et al. 2000Gangemi & Mika 2003 Kuhn 2001 Couclelis 2010 Voudouris 2010 Goodchild et al. 2007 Kjenstad 2006 Two different paradigms in geospatial representation. Spatial-primitives centered (left) and concept-centered (right). Source: M. Kavouras and M. Kokla (2008) Theories of Geographic Concepts, p. 296.

13. many commonalitiesamongtheseauthors

14. The same concepts are categorized differently depending on the context http://vissim.uwf.edu/VOTT/VOTT_desc.htm

15. ‘Ontologies of geographic information’* sense-perceptionsobservationsdata informationknowledgewisdom ??? ? At every step, we ask: “what is the meaning of_?” What gives information its meaning? How are data transformed into knowledge? Why model information and not directly the world? *Couclelis 2010, IJGIS, December

16. What gives information its meaning? • semantics on top of structure (syntax) • How are data transformed into knowledge? • by being integrated into some coherent story • Why model information and not directly the world? • Information entails a source and a decoder (agent)

17. Modeling information, not the world:three principles • Foregrounding the perspective of the user • Distinguishing a linked sequence layers of varying degrees of semantic richness • Selecting data through criteria resulting from the users’ purpose-oriented semantic choices

18. A representation is constructed in a particular way fora purpose weather maps forscientific study school text illustration TV weather forecast river models fornavigation company water resource agency cross-border regulation Purpose comes from the intentionality of the user a GIScience representation (model) is constructed in response to some user need

19. The popular DOLCE ontology

20. My 2010 framework: the static version The foundations information spacetime framework purpose The key ingredients spacetime granules classes of properties GI Constructs (GICs) Thestructure • representation levels Most ontologies are represented as trees or semi-lattices

21. This one is a lattice, with information and spacetimeframework at one end, and intentionality at the other The foundations information spacetime framework purpose The key ingredients spacetime granules classes of properties GI Constructs (GICs) Thestructure • representation levels • lattice

22. The foundations information spacetime framework purpose The key ingredients spacetime granules classes of properties GI Constructs (GICs) Thestructure representation levels • lattice Actually, it should be this way around

23. Geographic Information Constructs (GICs) topons, chronons, and codes across 7 property domains

24. The principle of semantic contraction • [

26. A somewhat similar idea from more practical folks… A review and assessment of land-use change models: dynamics of space, time, and human choice By Agarwal, Chetan; Green, Glen M.; Grove, J. Morgan; Evans, Tom P.; Schweik, Charles M. (2002) Gen. Tech. Rep. NE-297. Newton Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station.

27. The framework, 3 years later… • A micro-ontology generating engine?...

28. Ontology >> language for model design ontologies are models of models micro-theories are models a model is a micro-theory modeling is a language a model is a statement about the world language has semantics, syntax and pragmatics building a model is design-ing designed things reflect designer’s purpose purpose is supported by function

29. A structure emerges… Syntactics structure Model designer purpose perspective Semantics meaning Pragmatics context

30. Unpacking the ‘Ontologies’ framework Pragmatics Semantics Syntax SS context Purpose measurements context Patterns Data structures Interpretations Micro-ontologies

31. The temporal extension • One additional key ingredient: • R-event • For each level, a change in information that significantly alters the structure of GICs at that level • ‘significant’ is relative to purpose! • “Information: a difference that makes a difference”Gregory Bateson • And the R-event types by level are…

32. R-events change the context of the situation described • [

33. Adding uncertaintiesand times • [

34. Some features of the framework • Guides construction of micro-ontologies (and possibly process models) • Integrates design & analysis through user perspective • Adds context-relevant notions of time, change and uncertainty • Is compatible with much other work in geographic information science

35. And now, something more applied! • Geodesigning from the inside out

36. My advisor used to say… • “there is nothing as practical as a good theory” • Searching for practical solutions by becoming more abstract

37. What next?... Tentative, but a different way of looking at geospatial representation Continue connecting with literature Formalize! Try deriving micro-ontologies foruse with the Semantic Web Experiment with environmental and other process models