Building Ontology for
The National Map
Andrew Bulen, Jonathan Carter, Dalia Varanka
3rd Annual SOCoP Workshop, Reston, Virginia
December 3, 2010
U.S. Department of the Interior
U.S. Geological Survey
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
SOCoP Workshop, Nov. 2010
Outline




Topographic data conversion to triples
Geospatial relations for topographic data
Topographic feature ontology patterns
Conclusions
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: usercentered, object-centered, and environment-centered
FLOW
Water
water
Underground water
CAUSED
Crater (Circular-shaped depression
at the summit of a volcanic cone or
one on the surface of the land)
Crater (a manmade depression)
FORM
Crossing (A place where two or
more routes of transportation)
REMOVED
Mine (place where commercial
minerals)
Oilfield (area where petroleum
is/was)
flowTHROUGH
flowTHROUGH
flowTO
Arroyo (Watercourse or channel)
Channel (Linear deep part of a body
of water)
The surface of the Earth
causedBY
the impact of a meteorite
causedBY
an explosion
form
a junction or intersection (overpass,
underpass)
removedFROM
Earth
removedFROM
Earth
Feature Primitives: Properties that Meet
Necessary and Sufficient Conditions
Metals
RESOURCE
Industrial
Minerals
Surface
Mine
EXTRACTION
Underground
Mine
Required Relations Reflect Primitives
powers
Power lines
Buildings
powers
• offices
• maintenance sheds
• head frame (shaft) *
• ore processing
carriesTo
Conveyors
connects
Large vehicles
• haulers
• front-end loaders
• scoops
• dump trucks
carriesTo
Roads
• dirt / gravel
carriesTo/From
Disturbed ground
connects
• ore piles
• tailings
• quarry / pit **
• mountaintop removed **
Railroad
* Underground mining ** Surface mining
Complex Topographic Features

Component assemblages are supported by resource
systems and are embedded in the near-by landscape.
LANDSCAPE
SYSTEMS
COMPLEX
FEATURE
Complex Features, Systems, and
Landscapes
Material Services Corporation, Thornton, IL
Ü
1:24,000
0
0.5
1 Kilometers
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
usery@usgs.gov
Dalia Varanka
dvaranka@usgs.gov