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U.S. Department of the Interior U.S. Geological Survey Building Ontology for The National Map Andrew Bulen, Jonathan Carter, Dalia Varanka 3 rd Annual.

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Presentation on theme: "U.S. Department of the Interior U.S. Geological Survey Building Ontology for The National Map Andrew Bulen, Jonathan Carter, Dalia Varanka 3 rd Annual."— Presentation transcript:

1 U.S. Department of the Interior U.S. Geological Survey Building Ontology for The National Map Andrew Bulen, Jonathan Carter, Dalia Varanka 3 rd Annual SOCoP Workshop, Reston, Virginia December 3, 2010

2 Objectives To build a framework to more explicitly articulate detailed information about features contained in The National Map based on the semantics of feature types The flexible exchange of feature semantics enables more specific information access Richer data models based on ontology will Increase potential data applications

3 Project Description Develop a conceptual framework for data handling Develop algorithms for triples and ontology pattern concepts Build infrastructure and program digital products

4 Outline Topographic data conversion to triples Geospatial relations for topographic data Topographic feature ontology patterns Conclusions SOCoP Workshop, Nov. 2010

5 Data Conversion: Challenges Point data: the Geographic Names Information System (GNIS) gazetteer Vector data: hydrography, structures, transportation, divisions Challenges: Retrieving data from The National Map database formats Creating GML that is valid for any GML processing programs Linking data to features from other sources Converting large amounts of data

6 Data Conversion: Solutions Create an automated tool to translate existing format files Quantum GIS conversion to GML v2 with spatial reference system included in each geometry Store URI and point to resources replacing literals Parallelize conversion and spatial relation comparison

7 Conversion Tool: Jena and GeoTools libraries to convert to RDF

8 Configuration Editor

9 Data Conversion: Outcomes The semantic content of the converted data is identical to the original data. Increased openness, availability, and interoperability Data is simpler to extract Increased data richness Database size is very large

10 SPARQL Endpoint Challenges Create an endpoint so the public can access the data Must be fast, secure, easy to use Solutions User Virtuoso to create and serve the endpoint Virtuoso is capable of scaling to a large size Outcomes Data for converted areas is publicly accessible Data can be retrieved quickly Data is securely hosted

11 Spatial Relation Predicates: Challenges: Describe relation predicates between currently converted GIS data Build a vocabulary of relations for triples that can effectively model topography and topographic science Define relational predicates to meet standards

12 Spatial Relation Predicates Solutions: Define relations based on current USGS data models (Hydrologic Unit Codes, flow tables, etc.) Apply Open Geospatial Consortium (OGC) standard spatial relation terms Terms based on the 9-intersection model Determine new cognitive/linguistic spatial relations for topographic data

13 Vocabulary of Spatial Relations Topographic spatial relations and prepositions extracted from feature definitions Classified by logic types and spatial reference systems: user- centered, object-centered, and environment-centered FLOW WaterflowTHROUGHArroyo (Watercourse or channel) water flowTHROUGH Channel (Linear deep part of a body of water) Underground waterflowTOThe surface of the Earth CAUSED Crater (Circular-shaped depression at the summit of a volcanic cone or one on the surface of the land) causedBYthe impact of a meteorite Crater (a manmade depression) causedBY an explosion FORM Crossing (A place where two or more routes of transportation) form a junction or intersection (overpass, underpass) REMOVED Mine (place where commercial minerals) removedFROMEarth Oilfield (area where petroleum is/was) removedFROMEarth

14 Feature Primitives: Properties that Meet Necessary and Sufficient Conditions RESOURCE EXTRACTION Industrial Minerals Metals Surface Mine Underground Mine

15 Required Relations Reflect Primitives Power lines Conveyors Railroad Roads dirt / gravel Buildings offices maintenance sheds head frame (shaft) * ore processing Disturbed ground ore piles tailings quarry / pit ** mountaintop removed ** Large vehicles haulers front-end loaders scoops dump trucks * Underground mining** Surface mining connects powers carriesTo carriesTo/From carriesTo

16 Complex Topographic Features Component assemblages are supported by resource systems and are embedded in the near-by landscape. LANDSCAPE SYSTEMS COMPLEX FEATURE

17 Complex Features, Systems, and Landscapes 1:24,000 Material Services Corporation, Thornton, IL 01 Kilometers0.5

18 Topographic Science Modules

19 Complex Features and the Geosemantic Web Complex feature ontology saved to an ontology repository for re-use and customized by others queried and linked to other data for environmental applications

20 Topographic Ontology: Challenges Create ontology patterns so that necessary data can be linked using RDF and OWL Adhere to RDF and comparable research standards Build logical reasoning: template of definitions added for testing

21 Conclusions Our approach for semantic topographic data Converts features to RDF Identifies spatial relations that reflect feature primitives Uses a taxonomic structure that adds semantic specifics and offers relative scale Accounts for three stages of topographic representation

22 Outlook for 2011 Data made available to be queried and linked to other data for environmental applications Ontology saved to a repository for re-use and customized by others Gazetteer interface for data retrieval Use the GNIS data with spatial relations to advance gazetteer functions

23 Publications Varanka, D. and Usery, E.L., 2010, Special Section: Ontological Issues for The National Map: Cartographica: The International Journal for Geographic Information and Visualization, v. 45, n. 2, p Varanka, D., Carter, J., Shoberg, T., and Usery, E.L., in press, Topographic Mapping Data Semantics; Data Conversion and Enhancement, in Sheth, Amit and Ashish, Naveen, Eds., Geospatial Semantics and the Semantic Web. Semantic Web and Beyond: Computing for Human Experience, Springer. Varanka, D. and Jerris, T., 2010, Ontology Design Patterns for Complex Topographic Features. AutoCarto 2010, Orlando FL, November 15 – 18, Caro, H., and Varanka, D., Analysis of Spatial Relation Predicates in U.S. Geological Survey Feature Definitions. U.S. Geological Survey Open File Report.

24 Project Web Page Building Ontology for The National Map Principle Investigators E. Lynn Dalia

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