1. Ontologies for the Integration of Geospatial Data Michael Lutz Workshop: Semantics and Ontologies for GI Services, 2006 Paper: Lutz et al., Overcoming semantic heterogeneity in spatial data infrastructures, Computers and Geosciences (2008) With modification from Barbara Hofer

2. Context Spatial Data Infrastructures (SDI)  searching, accessing, integrating heterogeneous geographic data sets and GI services Syntactical basis: standards of the Open GIS Consortium (OGC) WMS, WFS, etc. Semantic heterogeneity causes problems Different classification schemes (e.g. for landuse or geological categories) in different countries or user communities

3. Semantic Heterogeneity Semantic heterogeneity occurs at three levels:  Metadata level: impedes the discovery of geographic datasets  Schema level: impedes the retrieval of datasets  Data content level: impedes the interpretation, integration and exchange of datasets

4. Example: Geological Maps Daten aus dem Kartenwerk Geologische Karte (DGK) des LAGB LSA, Geologische Grundkarte im Maßstab 1:25.000 Basis for engineering and hydro-geological decision making  different times  different authors  different areas  different classification systems  Semantic heterogeneity

5. Overcoming Semantic Heterogeneity Goal: Enable users to use a familiar vocabulary and translate to other classification schemes Approach: Use ontologies for making semantics of geospatial web services explicit  Hybrid ontology approach

6. Hybrid Ontology Approach Shared Vocabulary = One or several domain ontologies Especially domain ontologies should be property-centered, i.e. define properties and their ranges (and domains) Shared Vocabulary (property-centered) Application Ontology Existing Classification Scheme User-defined Classification Scheme Application Ontology Query Existing Classification Scheme provides vocabulary for define semantics for classes in

7. Hybrid Ontology Approach (2) How to: 1. Define “shared vocabulary” (aka “skeleton ontology”) 2. Define class definitions for each classification scheme based on shared vocabulary 3. Define query using the shared vocabulary or an existing classification scheme 4. Find similar or matching concepts for the query

8. Step 1: Define Shared Vocabulary For a Class&Concept:  Name Properties that describe the Class  Specify Fillers of the Properties -Find a common superclass that can be used as a range -Find subclasses for the individual fillers -Do they form value partitions?

9. Fine Sand Coarse Sand Medium Sand Shared Vocabulary ROCK Sand Clay Silt Carbonate Components hasAdditionalComponents hasMainComponents hasConsistency Consistency Storage isStored 1...30...* 1 0...1

10. Step 2: Class Definitions Based on Shared Vocabulary Many ontologies are simple is-a hierarchies  little flexibility for adding new concepts (or queries) To add this flexibility, properties (not classes) should be seen as the primary entities Concepts should be defined using existing properties  use cardinality constraints and value restrictions to further constrain the range of a role inside concept definitions

11. Application/Query Concept Loess Coarse Silt hasAddidionalComponentshasMainComponents n/a isStored 1...30...* 1 0...1 Loose Lime n/a isStored 0...1 hasConsistency

12. Step 3: Define Query Queries:  Class descriptions can be conceived as a query  Concepts that are subsumed by the query concept satisfy the query: “matchmaking”  …based on subsumption reasoning Two types of queries:  Simple queries  Defined queries

13. Types of Queries Simple Queries  Use an existing concept in one application ontology (i.e. a class in one classification system)  Look for matching (i.e. subsumed) concepts in other application ontologies  E.g. “show me all classes in your classification that correspond to my industrial complex class” Defined Queries  Use terms from the shared vocabulary to build a user- defined query concept  Look for matching (i.e. subsumed) concepts in all application ontologies  E.g. “show me all classes in your classification that have an inclination of less than 10% and have good transport connections”

14. Matching Concepts: Example

15. Assignment Goals: Get an idea how ontologies can be used for the integration of geospatial data Define a shared vocabulary for the domain of landcover classifications Define land use classes for e.g. CORINE land cover classification scheme Execute simple and defined queries

16. Assignment (2) Organisation & Teams: Teams of two; exercise one to be done alone; exercise two together. Pick a topic:  Artificial surfaces  Agricultural areas  Forest and seminatural areas  Wetlands  Water bodies Requested: presentation and report Questions in class: 14.01.2010 Presentation of assignment results: 28.01.2010

17. Exercise 1: Define a Shared Vocabulary Look at the CORINE land cover classification at http://terrestrial.eionet.europa.eu/CLC2000/classes or at another classification like Realraumanalyse at http://www.uni- klu.ac.at/geo/projekte/realraum/Typen.htm http://terrestrial.eionet.europa.eu/CLC2000/classes http://www.uni- klu.ac.at/geo/projekte/realraum/Typen.htm Pick a few classes/concepts (about 3) and try to come up with:  Properties that describe them  The “fillers” of these properties -Find a common superclass that can be used as a range -Find subclasses for the individual fillers -Do they form value partitions? (Little extra: Try to model these properties and filler classes in OWL  What kind of information is easy to map to OWL? What is more difficult?)

18. Exercise 2: Define Land Cover Classes Use two different land cover classification systems for one topic, e.g.: 1. CORINE 2. Realraumanalyse (http://www.uni- klu.ac.at/geo/projekte/realraum/Typen.htm) orhttp://www.uni- klu.ac.at/geo/projekte/realraum/Typen.htm New Zealand Land Cover Database http://www.mfe.govt.nz/issues/land/land-cover- dbase/classes.html etc. http://www.mfe.govt.nz/issues/land/land-cover- dbase/classes.html Use common shared vocabulary  Import skeleton ontology from the Harmonisa project into a new Protégé project Create defined classes for your classification system  Based on skeleton ontology Do simple and defined queries for your two ontologies  See common concepts in the two ontologies

19. Importing Ontologies Create and save a new Protégé project Import ontology

20. Importing Ontologies (2) Namespaces

21. Importing Ontologies (3)

22. Additional Reference Material Protégé OWL Tutorial:  Value partitions  Example for importing ontologies  Etc. Paper on Hybrid Ontology Approach by Lutz et al. 2008 Skeleton ontology of the Harmonisa project Material available on FTP server: ftp://ftp.geoinfo.tuwien.ac.at/courses/Ontology_08W/ ftp://ftp.geoinfo.tuwien.ac.at/courses/Ontology_08W/ - link on course website

23. Dataset 2 Dataset 1 equivalence or subsumption based on Domain Ontology Ontological (DL) description of the query concept “suitable for creating a business park” Query concept Application Ontology Concepts Ontologies for Enhanced GI Discovery Hybrid Ontology Approach Logical Reasoning Classification Scheme 2 Classification Scheme 1 Ontological (DL) description of the classes used in the classification Where are there areas that are suitable for creating a business park?

24. Example Application: Geological Maps (2) Goals: establish a service for semantic mapping between the different classification systems Enable user-specific property-based queries