geospatial ontologies and geosparql services
TRANSCRIPT
Copyright © 2014 Image Matters LLC. All rights reserved. | www.imagemattersllc.com
OGC OWS-10 Cross-Community Interoperability
Geospatial Ontologies and GeoSPARQL Services
OGC TC Meeting- Geosemantics WG
March 27, 2014
Stephane Fellah, Chief Knowledge Scientist
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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)
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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
The
Infocline
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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)
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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CrossingThe
Infocline
Physical
Logical
Conceptual
Data & Analytic Services
Business Apps
Data-centric services impose excessive cognitive load on
analysts
All Source All Source
Reduced
CognitiveLoad
Knowledge-assisted services enhance triage, fusion, dot-
connecting, pattern detection, inferencing
and sense making
Knowledge-assisted Semantic Services
Data-Centric Knowledge-Centric
A Paradigm Shift from Data-Centric to Knowledge-Centric
Business Apps
Data & Analytic Services
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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 Knowledge
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The train has already left the station…… an entire ecosystem of shared linked data exists
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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
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Semantic Mapping Components
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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)
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Core Geospatial Ontologies
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Mereology
Collections
Quality
Spatial Entities
Spatial Relations
Spatial Attributes
IdentifiersDatatypes
Upper ontology
Geometry
Measure
Reference Systems
Topology
EventTemporal Entities
Temporal Relations
Temporal RSSRS
Quantity
Temporal Quantity
Spatial Quantity
Temporal
Role
Event Relations
Measurement Scale
Spatial
Measure
Common
Math Entities
Math RelationsMath Ops
Math
Event
Utilities
Based upon solid theoretical foundations
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Semantic Gazetteer
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NGA Gazetteer
(Interactive Instrument)
Canadian Topo DB
(Compusult)
USGS Gazetteers
(Compusult)
WFS-G WFS-G WFS-G
GeoSPARQL Service
Semantic Mapping Component
Semantic Mapping Component
Semantic Mapping Component
Semantic Mapping Component
Geonames PostGIS
(Image Matters)
Gazetteer Mappings
RDF Store
Client
(Pyxis)
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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
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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
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Questions?
Contact Information
Stephane Fellah
Chief Knowledge Scientist
Image Matters LLC
Leesburg, VA
USA
+(703) 669 5510
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