Ontologies in Spatial Data Infrastructures Doug Nebert Federal Geographic Data Committee Reston, VA November 2009

2 Background Confused to about the meaning and utility of “spatial ontology” as this could be construed extremely narrowly as an enhanced gazetteer (problem solved!)

3 (Max Egenhofer)

4 How do geospatial communities use ontologies? Gazetteer, place name hierarchies Spatial operations Spatial relations, associations Vocabularies Spatial feature typology

5 Gazetteer interfaces

6 Gazetteers Include a set of landmark feature types and the names/identities of individual features within a type Geographic hierarchy may be managed or implied Alternate, official, historical, and other variant names may be managed Can be useful for orientation, refining search, providing geographic context

7 Which New York?

8 Spatial operations and relations In the context of performing geospatial analysis there is an ontology of operations (concepts) that are based on mathematical proofs There are contextual relational terms as well:  Near, far, adjacent  Passes under, over, through  Neighborhood, region  Along, beside

9 disjointcontainsequalinside meet coverscoveredByoverlap )(     )( ¬ ¬¬ ¬¬ ¬¬ ¬¬ ¬¬ ¬¬ ¬¬ ¬ )( ¬  ¬  )(  ¬  ¬  ¬  ¬  ¬  ¬  ¬  ¬ )( ¬    ¬   ¬  )( ¬       )( ¬  ¬   ¬   ¬  )( ¬   ¬  ¬  Egenhofer 4-intersection matrix Mathematically defines topological relations between objects and creates an actionable vocabulary

10 Vocabularies use of enumerations and code lists within a geospatial community is common to standardize and categorize resources Place code identifiers Coordinate reference systems Parameter (Attribute) value types Service types, standards, URNs

11 Geo-enabled data and codes There is abundant statistical data stored in tables with codes for the geography of interest: Address: street, city, state ZIP Code or ZIP+4 State/County/City code or name Congressional District

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13 H1N1 (PAHO)

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16 Feature (class) catalogue

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Semantic Based Knowledge Reasoning for Intelligent Search: Building a common GEOSS Ontology Abstraction of Classes and Attributes Building Interrelationship Domain Ontology Model ConceptualizationFacet mapping Current Knowledge Base: 500 Terminologies and Interrelationships 37 Logic Restrictions (35 Existential and 2 Universal) Wenwen Li, GMU Integral Components CEOS-Earth Observation Parameter INSPIRE-Theme INSPIRE-Spatial Ontology Type

19 GEOSS Ontology Snapshot Ontology (Spatial Object Type)Ontology (Theme)

20 GEOSS Ontology Snapshot-Contd. Ontology (CEOS) Cloud Type of CEOS

21 Ontology Improved Search

22 Geo-bridge for Meta-Catalogue Standards: Web Catalogue Service (CSW) – GOS, ESG Customized API – ECHO Web Interface – GCMD, NCDC Seamless Communication XML-encapsulated Request KVP-based Request Service Parser HTML parser XML parser Key Techniques Ajax: Asynchronous JavaScript and XML Multi-Threads Wenwen Li, GMU

23 Managing heterogeneity in GEO The construction of a common feature type/property ontology that joins terms from multiple discipline ontologies will allow for use and discovery of information across multiple domains and GEO “Societal Benefit Areas” Managing “observable” properties will allow joins between user or application requirements and available data and service resources

24 Bridging communities Arctic Spatial Data Infrastructure will support multi-disciplinary and multi-lingual map data search, management, and access Supports the Group on Earth Observations and the Arctic Council Candidate vocabularies: GEMET multi-lingual environmental thesaurus INSPIRE Feature Concept Catalog CEOS/NASA/NOAA Observation Types SWEET Ontology NASA GCMD/IDN Science Keywords