This document discusses moving from a data-centric to a knowledge-centric approach for geospatial data and services. It proposes using core geospatial ontologies and semantic technologies like OWL and SPARQL to encode conceptual models, business rules, and formal semantics. This would allow automated reasoning and flexible integration of data as knowledge is shared through a semantic layer. Example applications like semantic gazetteers are presented to illustrate how existing services could be enhanced by adding semantics. The document outlines next steps like standardizing core ontologies and developing semantic profiles and services. The overall approach aims to reduce costs and burdens on users by making geospatial data and knowledge more accessible and interpretable through shared formal representations.

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Sponsored by
Core Geospatial OntologiesCore Geospatial Ontologies
95th OGC Technical Committee
Boulder, Colorado USA
Stephane Fellah
03 June 2015
Copyright © 2015 Open Geospatial ConsortiumCopyright © 2015 Open Geospatial Consortium

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AgendaAgenda
Copyright © 2015 Open Geospatial Consortium

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Data-to-Knowledge Integration Services:Data-to-Knowledge Integration Services:
“Crossing the Infocline”“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.
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Value Proposition of Knowledge-Centric Approach (1)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

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Value Proposition of Knowledge-Centric Approach (2)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

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Physical
Logical
Conceptual
Data & Analytic Services
Business Apps
Data-centric services impose excessive
cognitive load on analysts
All Source All Source
R
educed
C
ognitive
Load
Knowledge-assisted Semantic
Services
Data-Centric Knowledge-Centric
A Paradigm Shift fromA Paradigm Shift from
Data-Centric to Knowledge-CentricData-Centric to Knowledge-Centric
Business Apps
Data & Analytic Services

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What Linked Data Is About?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
5 Rating for Linked Open Data★
Why Linked Open Data?
Semantics and Context

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

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Core Geospatial OntologiesCore Geospatial Ontologies
Copyright © 2015 Open Geospatial Consortium

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Geospatial Ontologies OverviewGeospatial 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)
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Semantic Gazetteers IntegrationSemantic Gazetteers Integration
Copyright © 2015 Open Geospatial Consortium
GNIS
WFS
GNIS
PostGIS RDBMS
Geonames
PostGIS RDBMS
Semantic Mapping
Engine
(GeoSPARQL->SQL)
Semantic Mapping
Engine
(GeoSPARQL->SQL)
Semantic Mapping
Engine
(GeoSPARQL->OGC Query)
GeoSPARQL Endpoint Linked Data REST API
Physical
Data
Layer
Knowledge
Layer
Web
Access
Layer
Mashup
Representation
Layer

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GeoSPARQL ClientGeoSPARQL Client
Copyright © 2015 Open Geospatial Consortium

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Proposed Roadmap: Next StepsProposed Roadmap: Next Steps
• Towards standardization process (W3C-OGC Spatial Data on
the Web WG)
– Prioritized by most useful microtheories
– Harmonization with other efforts
– Fast track publication by keeping microtheories minimal
• 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
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Key TakeawaysKey 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
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