Linking Ontologies to Spatial Databases Jenny Green & Catherine Dolbear

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

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

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)

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.

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!

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?

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.

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

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

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?

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

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!

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