Geospatial Ontologies and GeoSPARQL Services

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

http://www.imagemattersllc.com/

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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



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



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



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



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



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




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

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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

[email protected]

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