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.

U.S. Department of the InteriorU.S. Geological Survey

Building Ontology for The National Map

Andrew Bulen, Jonathan Carter, Dalia Varanka

3rd Annual SOCoP Workshop, Reston, Virginia

December 3, 2010

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

Project Description

Develop a conceptual framework for data handling

Develop algorithms for triples and ontology pattern concepts

Build infrastructure and program digital products

Outline

Topographic data conversion to triples Geospatial relations for topographic data Topographic feature ontology patterns Conclusions

SOCoP Workshop, Nov. 2010

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

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

Conversion Tool:Jena and GeoTools libraries to convert to RDF

Configuration Editor

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

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

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

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

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 Water flowTHROUGH Arroyo (Watercourse or channel)water flowTHROUGH Channel (Linear deep part of a body

of water)Underground water flowTO The surface of the EarthCAUSED Crater (Circular-shaped depression at the summit of a volcanic cone or one on the surface of the land)

causedBY the 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)

removedFROM Earth

Oilfield (area where petroleum is/was)

removedFROM Earth

Feature Primitives: Properties that Meet Necessary and Sufficient Conditions

RESOURCE EXTRACTION

Industrial Minerals

MetalsSurface

Mine

Underground

Mine

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

connects

powers

powers

carriesTo carriesTo/From

carriesTo

Complex Topographic Features

Component assemblages are supported by resource systems and are embedded in the near-by landscape.

LANDSCAPE

SYSTEMS

COMPLEXFEATURE

Complex Features, Systems, and Landscapes

1:24,000

Material Services Corporation, Thornton, IL

0 1 Kilometers0.5

Ü

Topographic Science Modules

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

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

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

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

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. 103-104.

 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, 2010.

Caro, H., and Varanka, D., Analysis of Spatial Relation Predicates in U.S. Geological Survey Feature Definitions. U.S. Geological Survey Open File Report.

Project Web Page

Building Ontology for The National Map

http://cegis.usgs.gov/ontology.html

Principle Investigators

E. Lynn Usery [email protected]

Dalia Varanka [email protected]