1. Linking Ontologies to Spatial Databases
Jenny Green & Catherine Dolbear

2. Agenda Ordnance Survey – Who we are
Semantic Research – Our motivations and goals
Linking Ontologies to Spatial Databases
Difficulties
Our approach
Conclusions

3. Ordnance Survey – Who we are
National Mapping Agency of Great Britain
Data vendor: one of the largest geospatial databases in the world
Customers use GIS systems & spatially enabled databases to process data
Concepts – far more variable in geography than in medicine

4. Motivation for Semantic Research
Describe the content of our database explicitly.
Allow product customisation.
Improve integration of our data with our customers’.
Ordnance Survey
Valuation Office
Has Form Education Services
School and Premises
School
Local Authority School
Junior School
High School
Infant School
Public & Independent School
Private Primary School
Private Secondary School
Current processes are manual and ad hoc (example diagram of how OS has integrated tax office data into our Address Layer product – lot of reading manuals and guessing)

5. Current Data Integration Issues
Syntactic / structural differences
Differing database schemas.
Various transfer formats.
Continuity of terms used between databases
Semantic differences:
Between the domains.
Between a domain and the data in the database.

6. Linking Ontologies to Spatial Databases
Database schemas rarely good descriptions of the domain.
Based on initial design constraints.
Performance optimisation processes.
Maintenance history.
Relevant relationships buried in software or attribute encoding.
Semantics promise to bring hidden complexity into the open.
Mapping from data to domain encoded in a data ontology.
Database schemas rarely good descriptions of the domain (as users understand them)
If databases that orderly and easy to understand, semantics wouldn’t be needed!

7. Creating a Mapping Data Ontology – describes the database schema.
Create mappings between the data ontology and the domain ontology.
Spatial Data presents an added intricacy.
How do we combine Space and Semantics?

8. Mapping Between Viewpoints – The Data Ontology
‘River Stretch’ – not explicit in our database
Linear segments of ‘Water’
‘Floodplain’
Area of Land touching a River
So the problem is that “River Stretch” isn’t explicitly termed in our database: it’s those linear segments of “Inland Water” – another use of ontologies is to derive data on-the-fly from a relational database, and something like a “Floodplain” may be that area of land adjacent to a waterbody – here the derivation requires spatial information.
Data ontology needs to contain information about which database table the information about the user’s concept is in, and what particular columns must contain in order for that row to be an instance of the user’s concept. E.g. A Road would be any row in the Topographic Area Table with the Theme column taking the value “Roads, Tracks And Paths”. Since there is also spatial information, we can derive information about the “linearity” of the polygon (although this may best be done offline). The data ontology would also contain information about a River being made up of River Stretches, and Floodplains being areas of land (Theme Column contains “Land”) spatially touching (an Oracle SQL operator) a River Stretch.

9. Current Technologies
D2RQ - maps SPARQL queries to SQL, creating “virtual” RDF [Bizer et al, 2006]
No need to convert data to RDF explicitly
But assumes generation of an ontology from the database schema
For content customisation, modifying the API to:
Use the data ontology mapping
Map queries via spatial relations to SQL spatial operators
D2RQ assumes generation of an ontology from the database schema, but for content customisation, we actually go the other way round

10. Relational (Spatial) Database
System Overview
Query
Domain ontology
OWL Inference Engine
Virtual RDF Graph
D2RQ Mapping
OS Mapping
SQL + functions
SQL + functions
Relational (Spatial) Database

11. System Overview (cont)…
Spatial databases are not normalised databases.
Mappings between the database and ontology concepts are not a one to one mapping.
Functions need to be included in the mapping.
Issues with the complexity of the mapping
Web services for complex processing?
Specify views within the data ontology or more complex function calls?
Some compromise on reformulating the relational data?

12. Example Use Case: Water Pollution
Query: Find all river stretches which have decreased chemical water quality.
OS Hydrology Domain Ontology
Environment Agency
Domain Ontology
Merged ontology
OS Data Ontology
EA Data Ontology
OS MasterMap
Environment Agency Data

13. Conclusions
Ontologies auto-generated from database schemas are NOT sufficient & don’t address the real problem of semantics.
Simple relations between the domain ontology and the database schema are not sufficient.
Queries over OWL ontologies need to be more complete/easier. (we await the release of SparQL-DL)
Speed will become an issue as the system develops.
There is no simple solution!

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Questions
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