Geospatial Ontologies and GeoSPARQL Services

OGC OWS-10
Cross-Community Interoperability
Geospatial Ontologies and
GeoSPARQL Services
OGC TC Meeting- Geosemantics WG
March 27, 2014
Stephane Fellah, Chief Knowledge Scientist
Copyright © 2014 Image Matters LLC. All rights reserved. | www.imagemattersllc.com

Objectives of Our Research & Development
• Prove core concepts and state of readiness of
semantic-based approaches to interoperability
• Build, test and demonstrate Core Geospatial
Ontology
– Key new building block in achieving geospatial
interoperability
– Enabler for producing and sharing Geospatial Linked Data
(shared geospatial knowledge)
• Implement a true semantic-enabled service that
demonstrates semantic integration and
interoperability across disparate geospatial sources
(a prototype “Semantic Gazetteer”; featuring
GeoSPARQL)
Page 2

Data-to-Knowledge Integration Services:
“Crossing the Infocline”
Data-Centric World
(Today)
• Unsustainable cognitive load on
user to fuse, interpret and make
sense of data
• Interoperability is brittle, error-prone
and restricted due to lack of
formal semantics
• High cost of integration
Knowledge-Centric World
(Our Goal)
• Semantic-enabled services reduce
burden by “knowledge-assisting” user
• Semantic layer provides unambiguous
interpretations and uniformity… “last
rung in interoperability ladder”
• Agile, fast and low cost integration.
3

Page 4
Value Proposition of Knowledge-Centric Approach (1)
Issues with Current Data-
Centric Approaches
Knowledge-Centric Approach Increased Value
Data model standardization relies
upon homogeneous data
description and organization.
Employs a standards-based formal, sharable
framework that provides a conceptual domain
model to accommodate various business needs.
• Allows decentralized extensions of the domain
model
• Accommodates heterogeneous
implementations of the domain model (lessens
impact on systems; reduces cost)
• Shareable machine-processable model and
business rules; reduces required code base
Increases the chance for multiple
interpretations and
misinterpretations of data.
Encodes data characteristics in ontology.
• Increased software maintainability
• Improved data interpretation and utility
• Actionable information for the decision maker
Data model implementations have
limited support for business rules,
and lack expressiveness.
Standards-based knowledge encoding (OWL,
SPARQL Rules) captures formal conceptual
models and business rules, providing explicit,
unambiguous meanings for use in automated
systems.
• Reduction of software and associated
development cost
• Conceptual models and rules that provide
enhanced meaning, thus reducing the burden
on users
• Unambiguous interpretation of domain model;
greater consistency in use
Presumes a priori knowledge of
data utility. Semantics are pre-wired
into applications based
upon data verbosity, conditions
and constraints.
Encoding the conceptual model and rules
explicitly using OWL enables rapid integration of
new/changed data. Software accesses data
through the “knowledge layer” where it’s easier to
accommodate changes without rewriting software.
• Reduced software maintenance due to data
perturbations
• Software quickly adapts to evolving domain
model
• New information are readily introduced and
understood in their broader domain context

Page 5
Value Proposition of Knowledge-Centric Approach (2)
Issues with Current Data-Centric
Approaches
Knowledge-Centric Approach Increased Value
Implementations are inflexible when data
requirements change. Whenever business
rules and semantic meaning are encoded in a
programming language, changes impact the
full development life cycle for software and
data..
Uses an ontology that contains a flexible,
versatile conceptual model that can
better accommodate the requirements of
each stakeholder in the business
domain.
• Increased flexibility to accommodate
stakeholder needs; Decentralized and
organic evolution of the domain model
• Changes only impact affected stakeholders,
not others; reduces software updates
• Software adapts to domain model as
ontology evolves
• The enterprise can better keep up with
changing environment/requirements
Requires that data inferencing and validation
rules are encoded in software, or delegated
to human-intensive validation processes.
Uses a formal language (OWL) that
provides well-defined semantics in a
form compliant with off-the-shelf software
that automates data inferencing and
validation.
• Employs off-the-shelf software for
inferencing and validation
• Reduction of validation and testing in the
development process
• Uses all available data from sources,
including inferences, while accommodating
cases of missing/incomplete information

A Paradigm Shift from
Data-Centric to Knowledge-Centric
Data-centric services impose
excessive cognitive load on
analysts
Business Apps
Knowledge-assisted services
enhance triage, fusion, dot-connecting,
Business Apps
Data & Analytic Services
Page 6
Crossing
The
Infocline
Conceptual
Logical
Physical
Data & Analytic Services
All Source All Source
pattern detection,
inferencing
and sense making
Knowledge-assisted
Semantic Services
Data-Centric Knowledge-Centric

Why Linked Open Data?
5 ★ Rating for Linked Open Data
Page 7
What Linked Data Is About?
Tim Berners-Lee Vision: “… It’s not just about putting data on the web. It
is about making links, so that a person or machine can explore the web of
data. With linked data, when you have some of it, you can find other
related data.” By adding formal semantics and context to Linked Data,
it becomes “understandable” by software.
For the web to remain robust and grow, the following rules (standards) must apply:
• Use URIs as names for things
• Use HTTP URIs so that people can look up those names
• When someone looks up a URI, provide useful information, using the
standards (RDF, OWL, SPARQL)
• Include links to other URIs so that they can discover more things.
★ Available on the web
★★ Available as machine-readable structured data
★★★ Non-proprietary format
★★★★ Use open standards from W3C (RDF and SPARQL)
★★★★★ Link your data to other people’s data to provide context
Semantics and Context

Vision: Towards a Web of Shared Knowledge
The train has already left the station…… an entire ecosystem of shared linked data exists
Page 8

Summary of IM Contributions to OWS-10
• Geospatial ontologies
• Incident ontology
• Semantic service components
• Prototype “Semantic Gazetteer” service (GeoSPARQL-enabled)
that unifies access to 5 gazetteer sources – produces
“geospatial linked data” that can be shared across the Web
Page 9

Semantic Mapping Components
Page 10

Geospatial Ontologies Overview
• In-kind contribution from Image Matters to OGC community (8+ years of
development and testing)
• Core cross-domain geospatial ontologies
• Candidate foundational ontologies to bootstrap the Geospatial Semantic
Web
• Design criteria:
– Minimalist semantic commitment
– Modular
– Extensible
– Reusable
– Cross-domain
– Leverage existing standards
• Benefits
– Multilingual support
– Linkable to other domains
– Sharable and machine-processable
– etc. (see slides 5 & 6)
Page 11

Core Geospatial Ontologies
Temporal Relations Temporal Entities Event
Math
Math Entities
Math Relations
Math Ops
Page 12
Mereology
Collections
Quality
Spatial Entities
Spatial Attributes Spatial Relations
Identifiers
Datatypes
Upper ontology
Geometry
Measure
Reference Systems
Topology
SRS Temporal RS
Quantity
Temporal Quantity
Spatial Quantity
Temporal
Role
Event Relations
Measurement Scale
Spatial
Measure
Common
Event
Utilities
Based upon solid theoretical foundations

Semantic Gazetteer
WFS-G WFS-G WFS-G
Page 13
NGA Gazetteer
(Interactive Instrument)
Canadian Topo DB
(Compusult)
USGS Gazetteers
(Compusult)
GeoSPARQL Service
Semantic Mapping
Component
Semantic Mapping
Component
Semantic Mapping
Component
Semantic Mapping
Component
Geonames PostGIS
(Image Matters)
Gazetteer Mappings
RDF Store
Client
(Pyxis)

Proposed Roadmap: Next Steps
• Towards standardization process
• Exercise the robustness of these core geospatial
ontologies by developing profiles for different vertical
domains
• Semantic-enablement of existing OGC web services
• Define architecture for Semantic Geospatial Services
leveraging the core geospatial ontologies and existing
Linked Data standards
Page 14

Key Takeaways
• The core concepts espoused herein are solid
and repeatable
• Semantic-based interoperability can be
achieved with current technology
• A Core Geospatial Ontology is foundational to
sharing geospatial data and knowledge
• Semantic Gazetteers, and many other such
services, illustrate the power and value of
semantic-based interoperability and services
– Can be readily added to existing “data-centric”
infrastructure
Page 15

Questions?
Contact Information
Stephane Fellah
Chief Knowledge Scientist
Image Matters LLC
Leesburg, VA
USA
+(703) 669 5510
stephanef@imagemattersllc.com
Page 16

Geospatial Ontologies and GeoSPARQL Services

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