Ontologies for the Integration of Geospatial Data

Michael LutzWorkshop: 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

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

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

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-geologicaldecision making

different times

different authors

different areas

different classification systems

Semantic heterogeneity

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

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)

ApplicationOntology

ExistingClassification

Scheme

User-definedClassification

Scheme

ApplicationOntology

Query

ExistingClassification

Scheme

provides vocabulary for

define semantics for classes in

Hybrid Ontology Approach (2)

How to:1. Define “shared vocabulary” (aka “skeleton

ontology”)2. Define class definitions for each classification

scheme based on shared vocabulary3. Define query using the shared vocabulary or an

existing classification scheme4. Find similar or matching concepts for the query

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?

Fine Sand

Coarse Sand

Medium Sand

Shared Vocabulary

ROCK

Sand

Clay

Silt

CarbonateComponents

hasAdditionalComponentshasMainComponents

hasConsistency

Consistency

StorageisStored

1...3 0...*

1

0...1

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

Application/Query Concept

Loess

Coarse Silt

hasAddidionalComponentshasMainComponents

n/aisStored

1...3 0...*

1

0...1

Loose

Lime

n/a isStored

0...1

hasConsistency

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

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”

Matching Concepts: Example

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

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

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

• 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?)

Exercise 2: Define Land Cover Classes

Use two different land cover classification systems for

one topic, e.g.:1. CORINE2. Realraumanalyse (

http://www.uni-klu.ac.at/geo/projekte/realraum/Typen.htm ) or New Zealand Land Cover Database http://www.mfe.govt.nz/issues/land/land-cover-dbase/classes.html etc.

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

Importing Ontologies

• Create and save a new Protégé project

Import ontology

Importing Ontologies (2)

Namespaces

Importing Ontologies (3)

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/

- link on course website