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

4. Valuation Office Ordnance Survey Valuation Office Ordnance Survey Has Form Education Services Infant School Local Authority School School and Premises School High School Public & Independent School Junior School Private Secondary School Private Primary School Motivation for Semantic Research • Describe the content of our database explicitly. • Allow product customisation. • Improve integration of our data with our customers’.

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.

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

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

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

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. EA Data Ontology OS Data Ontology 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 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!

14. Questions Thank you for your attention for further details see: http://www.ordnancesurvey.co.uk/ontology