ontologies for emergency & disaster management

Copyright © 2014 Image Matters LLC. All rights reserved. | www.imagemattersllc.com

OGC OWS-10

Cross-Community Interoperability

Ontologies for Emergency & Disaster Management(The application of geospatial linked data)

March 25, 2014

Stephane Fellah, Chief Knowledge Scientist

http://www.imagemattersllc.com/


Outline

• Summary of our approach

• Core Geospatial Ontology

• Core Incident Ontology for E&DM

• Next steps

• Key takeaways

Page 2



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)

• Build, test and demonstrate core ontologies to enable

E&DM interoperability

• Implement a true semantic-enabled service that

demonstrates semantic integration and

interoperability across disparate geospatial sources

(a prototype “Semantic Gazetteer”)

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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.

4


Copyright © 2014 Image Matters LLC. All rights reserved. | www.imagemattersllc.comPage 5

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



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



Crossing

The

Infocline

Physical

Logical

Conceptual

Data & Analytic Services

Business Apps

Data-centric services impose

excessive cognitive load on

analysts

All Source All Source

Knowledge-assisted services

enhance triage, fusion, dot-

connecting, pattern detection,

inferencing

and sense making

Knowledge–assistedSemantic 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

Page 9

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

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 foundation 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

Page 12

Mereology

Collections

Quality

Spatial Entities

Spatial RelationsSpatial Attributes

IdentifiersDatatypes

Upper ontology

Geometry

Measure

Reference Systems

Topology

EventTemporal EntitiesTemporal 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

Page 13

NGA Gazetteer

(Interactive Instrument)

Canadian Topo DB

(Compusult)

USGS Gazetteers

(Compusult)

WFS-G WFS-G WFS-G

GeoSPARQL Service

Semantic Mapping

ComponentSemantic Mapping

Component

Semantic Mapping

Component

Semantic Mapping

Component

Geonames PostGIS

(Image Matters)

Gazetteer Mappings

RDF Store

Client

(Pyxis)



Emergency Management Challenges

• Analysts and operators need to quickly triage, fuse,

connect dots, detect patterns, infer insights, and

make sense of the flow of incident information to get

an unambiguous Common Operational Picture

• Need to integrate and interpret incidents,

observations, mutual aid requests, alerts, etc.

generated across a multi-agency, multi-jurisdictional

spectrum

• Limited interoperability between agencies due to

different protocols, taxonomies, models and

representations (stovepipes are still there!)

Page 14



Incident Model Design Tenets

• Define a Core Incident Model applicable across domains, agencies and

jurisdictions

• Minimal semantic commitment (focus on core concepts and properties)

• Leverage Core Geospatial Ontologies

• Accommodate different Incident Model Profiles and Taxonomies

• Use Linked Data standards (RDF, SPARQL, RDFS, OWL, LDP)

– OWL used to model ontologies

– SKOS used to model taxonomies

• Sharable and machine-processable

• Linkable to other domains

• Multilingual support

Page 15



Incident Model

Page 16



Unified Semantic Incident Model

Page 17

NIEM

EXDL

NDEX

Unified

Incident model

NIEM

Converter

EDXL

Converter

NDEX

Converter

Other

Converter

NIEM

EDXL

NDEX

NIEM

View

EDXL

View

NDEX

View

Other

ViewOther Other

Profile1

Profile2

Profile3

Core

Incident

Model

Incident Data

Store

Data to knowledge Mapping



LEAPS Datasets

• Homeland Security Working Group

– Encoded taxonomies of Incident and Natural Events in SKOS

– Encoded symbols using Point Symbology ontology

• OpenFEMA disaster summaries

– Encoded disaster summary in linked data

• 911 Seattle Police dataset

– Encoded incident data as linked data

• Worldwide Incident Tracking System (WITS)

– Encoded Terrorist Incidents as linked data

• Abu Dhabi Police Model

Biggest challenge was the lack of good quality data.

Page 18



Homeland Security Work Group Symbology

Page 19



Proposed Roadmap: Next Steps

• Towards comprehensive Core Emergency Management Ontologies:

– Ontology for Organizations, covering governmental and non-governmental organizations involved in

E&DM; jurisdictions, roles and responsibilities of agencies and staff, type of resources they can provide

(based on W3C organization ontology)

– Ontology for Resource Management: Characterization of EM resources, including personnel and

equipment (see NIMS standard)

– Ontology for Response Activities: Covers aspects related to workflows of EM activities and services

such as medical services, aid delivery, food and shelter, victim triaging, on-site treatment, transportation

– Ontology for Communication: Covers communications (request, response, acknowledgment, alert, etc.)

and E&DM related message types (request/response for status, resources, information, deployment,

quote, requisitions, etc). (see EDXL-RM)

• Exercise the robustness of Core EM ontologies by developing profiles for different emergency-related

domains, agencies and jurisdictions

• Define architecture for Semantic Emergency Management System leveraging the Core EM ontologies

• Implement services to perform semantic mediation of incident information and representations

• Investigate, develop and test reasoning and inference for Incident/Resource/Communication Management

• Call for Datasets at International/Federal/State/Local levels

Page 20



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

• Core E&DM ontologies are crucial to interoperability

• 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 21



Questions?

Contact Information

Stephane Fellah

Chief Knowledge Scientist

Image Matters LLC

Leesburg, VA

USA

+(703) 669 5510

[email protected]



Homeland Security Work Group Symbology
