© Copyright 2001-2005, TopQuadrant Inc. "Semantic Technology and Ontology Engineering for Enterprise Architecture" Ralph Hodgson, TopQuadrant Enterprise

© Copyright 2001-2005, TopQuadrant Inc.

"Semantic Technology and Ontology Engineering for Enterprise Architecture"

Ralph Hodgson, TopQuadrant

Enterprise Architecture SummitMay 22-24, Miami, Florida.

© Copyright 2001 -2005 TopQuadrant Inc., “Semantic Technology and EA”, slide 2

TopQuadrant

Introductions – Ralph Hodgson

Object Technologist since 1982 Came to US in 1994 to create IBM’s Object Technology

Practice Founding member of IBM’s Java and Emerging Technology

Practice and IBM’s Portal Practice Co-founder of TopQuadrant, Inc. in 2001

leading research consultancy and trusted intermediary for the intelligent application of semantic technologies

Recent work: NASA on Space Engineering Ontologies for model-based life-cycle support GSA on FEA-RMO ontologies

Ralph Hodgson TopQuadrant, Inc.

E-mail: [email protected]

(703) 960-1028


TopQuadrant

Model-Based Support for Vehicle and Mission Support Life-Cycles

Capture constraints

Expand to createDetailed model

Simulation & Test

What if Scenario (invention)

RequirementsDefinition

Operate / UpgradeBuild/TestDesign

Model-based Procurement

Typical lifecycle

Model-based Operations &

Sustaining Engineering


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The Problems of Engineering Complex Systems

NASA andContractorPersonnel

NASA Systems

EOSDSKSC PRACA JSC PRACA GFE PRACA SEDS MRCS CVAS CVWAVETAIR

System Lifecycle

Operate

MaintainUpgrad

e

Design

Manufacture

Test

Learn

Acquisition: Analysis, Trades, Decision Support, Lessons Learned

Command and Control, Incident Management

System Engineering: Reliability, Performance, Risk Mitigation, Impact Analysis, FMEA

Acquisition: Analysis, Trades, Decision Support, Lessons Learned


TopQuadrant

Our Worldview

Too many applications are being built with proprietary structures that are non-interoperable

Many are busy mapping across islands using at best databases, and XML, but at worst documents and spreadsheets

Semantic technology is a key enabler for realizing the renewed vision of space exploration: System interoperability Model-based systems engineering Organizational memory Knowledge reuse


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The Solution

Ontology Architecture Enterprise-wide, Lifecycle-wide, “Incremental and Iterative”

Ontology Models Model-based support of engineering activities - multi-discipline

Build Semantic Engine Infrastructure Needs you to

start learning, start thinking “federated”, deploy early and often

NASA Knowledge sources

EOSDSKSC PRACA JSC PRACA GFE PRACA JEEVES MRCS CVAS CVSEDS

NASA Knowledge Hub

Knowledge AdvisorProfiles

Space Engineeri

ng Ontologie

s

TAIR


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Coverage

NASA OntologiesNASA Ontology ExamplesExamples of UseCOVE - Collaborative

Ontology Visualization and Evolution environment

iLoc - Insight LocatorSemantic Enterprise

ArchitectureSemantic IT Governance


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NASA Ontologies

Ontology Description Example conceptsnasa_core Common root

concepts across all of NASA

Artifact, Activity, Document, Initiative, Mission, Organization, Technology, Vehicle

nasa_ea Enterprise architecture concepts

Enterprise, Governance, Process, Role, Process, Task, Workproduct

nasa_system System concepts System, Component, Capability, Function, Behavior, Intent

nasa_technology

Specific technologies of interest to NASA

Software Technology, Propulsion, Power, Thermal Protection, Life Support Technology

nasa_discipline Nasa disciplines Electrical Engineering, Mechanical Engineering, Space Engineering, Thermal Dynamics, …

nasa_sba Simulation-based acquisition

Cost, Performance, RFI, RFP, Risk, Tradeoff

… … …


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Models and Namespaces:NASA Ontologies

Enterprise InformationTechnology

Structure, Electrical, Hydraulic, Thermal, …

LifecycleSocial

Competencies Critical Skills Human

Organizational Risks


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NASA CORE – common foundation


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NASA System Ontology

SBF(I) model –

System has structure, behavior, function and intent


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Example of Space Shuttle Ontology


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NASA Enterprise Ontology

Document-Centric Knowledge-Centric


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Some associations in the NASA Enterprise Architecture Process Ontology

Task

Goal

Role Measure

Agent

Process


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Soon you have many ontologies to manage

The NASA models: content and schema dependencies and governance


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Content OWL files –Automated WADS Engine (AWE)

The Automated WADS Environment (AWE) solution uses a knowledge model in the form of an Ontology with rules to aggregate and process data from existing multiple electrical component databases.

Schema dependencies


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Operations

Governance and Learning

AWE

AWE Integrates all Stakeholders, Information Sources and Processes

Maintain Rules

Constructs and updates rules

Uses AWE’s rules to generate WADs

OEL Engineer

NASA OEL Engineer

Generate WAD

Evolve MLOs and MRs

PR WAD

Design Center System Engineer

Rules Engine

Knowledge Model

Document Generator

Interoperability Engine

MLOs and MRs

Revise for New

Contraints, Lessons Learned

OEL System Engineer

Constructs and updates MLOs

Document Routing


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Ontology Lifecycle Management Environments - COVE and TopBraid respond to the need:

“How can I deploy simple web forms to my team” “How can I manage and version control multiple

ontologies” “How can I administer who can contribute data to

our ontologies” “How can I move concepts between name spaces

and propagate the change to all dependent ontologies”

“How can I manage the provenance of who contributed what”

“How do I bring together different content sources from different contexts”


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Ontology Lifecycle Management Environments

Schemas Content

Use

COVE

TopBRAID

Prot

égé SW

OOP

EXCEL

Export

FeedbackFeedback

XML

Views

Views

Bridges

Views


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Features of COVE and TopBRAID:

Thin-Client

Web access for entry of instance data

Ontology Management

Datasets

Ontology-Driven entry forms

TopBraid – RDF Gateway ServerCOVE - Websphere Portal

Select one or more ontologiesSelect subset of classesAdd instance dataDownload one or more OWL files. Control access of files.Import/Export with ontology editors

e.g.: Protégé or SWOOPLoad triplesConvert Excel Spreadsheets, XML, Databases,

and TaxonomiesAssistance in merging ontologies Re-factoring across namespacesConstruct Ontology BridgesSave modified Ontologies as RDF or OWL files. Import/Export of DatasetsMerge DatasetsRights to view, edit and manage files

assigned at the user or group level.

Full form mode, or Express entry of instancesTabular entryViewpoints


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TopBraid™: Dataset Builder


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TopBraid™: Ontologies and Datasets


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NASA COVE – Browsing Classes

Merged Ontologies

Select transitive relation for tree view


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NASA COVE – Creating a Directorate

Directorates from NASA taxonomy


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Features of COVE and TopBraid – Dynamic Hierarchies

Instances of ‘board’ being authored at KSC


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TopBRAID – Ontology-Driven Forms

Instances of ‘Application’ being authored at KSC


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COVE Lessons

Schema work and content work are different Schemas are hard work – verification and validation Content is everywhere

Model Management is key Viewpoints Dependencies Bridges Lens

Model-building is collaborative Communities are key Dependencies Feedback and Feed-forward

Governance is essential – does not mean centralization but facilitation Namespace management Model merging and splitting Notifications


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NASA’s Ontology-Based Insight Locator

Ontology-Based Environments are Collaborative Environments

iLoc is a semantic-based multi-peer environment (p2p, c/s) for model-based collaborative work


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Many needs for Semantic Collaborative Environments

Ontology Lifecycle Management Environments

System Engineers Workbench

Mission Planners Workbench

Community of Practice Environments

Enterprise Architects Workbench

IT Governance Workbench

TopBRAID, COVE iLoc-OE

iLoc-SE

iLoc-MP

SCOPE

TopSCAPE-EA™

TopSCAPE-ITG™


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Platform Infrastructure

Workgroup Enablement

Semantic Collaborative Environment Architecture

Virtual Project Room

Realtime Collaboration

Alerts

Member Awareness

Workspace

Social Networks

Meetings

Roles

Work Settings

Choreography

Artifacts

Application Sharing

Activities

Tools

Tools Registry

WhiteboardDecision Support

EditorsQuery Manager

Knowledge Enablement

Ontology Registry

ArchivalCase Library

CategorizationSearch

Graphics3D-Engine2D-Engine GIS

Event Management TimelinesCalendar

Semantic Infrastructure Semantic Engine

p2p Metadata ReplicatorRSS

Triple Store Remote Sync

Eclipse JXTA

JENA

Semantic Blogs


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Front-End of the Life-Cycle:Simulation-Based Acquisition

Pro

posa

ls

Sco

pe A

nnota

te

Valid

ate

NEXiOM Models•feedback re. quality•feedback re. IDTs

•feedback re. relevancefor strategic planning,capital planning, risk

management and partnering

Discipline-Based Tooling

Decisions and recommendations for

improvement and partnering

Ontology-Based

ProposalRepository

Assessment and Trades

C. Potential for reuse of Technologies and Components

A. Program area supported?

B. Assess Performance, Risk and Cost across disciplines?

Proposal Assessment

Onto

logy-B

ase

d Im

port

D. Synergies for partnering


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Ele

ctr

ica

l P

ow

er

An

aly

sis

iLoc Simulation-Based Acquisition Environment

IDT DB

TopSCAPEDisciplineOntology Models

NExIOM Ontology Models

SI

T2

SI

SI

T1

RFx DB

WS

WS

WS

WS

Mapping

Mapping

Mapping

Mapping

Translation Models

IL

AL

BL

IL

AL

BL

SI

W S

Semantic ApplicationInteraction LogicApplication LogicSemantic Interface

Co

st

Mo

de

lin

g

COVE

W S

Ontology Authoring

Pe

rfo

rma

nc

e

Mo

de

lin

g

Ris

k M

od

eli

ng

Tra

de

-Off

s

An

aly

sis

Str

uc

ture

an

d

Co

nn

ec

tiv

ity

Ele

ctr

ica

l P

ow

er

An

aly

sis

W S W S W S W S W S


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Operations End of the Life-cycle – Semantic Command and Control Environment (SCCE)

screenshots from Sherrill-Lubinski’s SL-GMS

The Launch Controller (LC) looks for weather and ice conditions that can affect the checkout for launch, using a high-level summary-level dashboard, called the ICE display.

A condition occurs in the fueling operations and LC asks an engineer in the firing room to look into fuelling operations.

The Firing Room Engineer (FRE) has a more detailed view of the fueling operation and notices some alarm messages on the hypergolic flows.

FRE asks a Hydraulics Engineer to monitor a dashboard of the Fuel Tank.

The Hydraulics Engineer switches to a more detail view of the hypergolic fluids supplies and discovers a leaking valve problem.


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Semantic Command and Control:Knowledge-Based Capabilities


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Semantic Command and Control:Conceptual Architecture

Collaborative Mission ControlKnowledge

Base

Semantic Engine

SCCE Capabilities

Launch Data Bus

NASA Networks

NASA Grid


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Business Management

Organization

Business Activity

Business Process

Capability

Resource

Semantic Enterprise Architecture

Business Environment


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Semantic Enterprise Architecture Environments: Lifting the “lid on the enterprise”

Enterprise Architecture is a “System of

Systems”


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Ontology Approach to Enterprise Architecture

Bridges the gaps between business, technology and IT

Makes Value Nets “Navigate-able”

Makes Capabilities “Knowledge-able”

Makes Components “Knowledge-able”

Uses Semantic-Enabled Collaborative Tools

Component knows:where it is used, how it is realized, what it depends on,its measures of effectiveness

Knowledge Model using Semantic TechnologyBehavior Model for inferencingFederated ArchitectureAnalytical ToolsDecision Support

Capability knows:why it exists,what enterprise activities need it used, what it depends on, its measures of effectiveness

“Line of Sight” across:extended enterprise business unitswithin business unitsto measures of effectiveness

“Connects the dots” across:Business, technology and IT models


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Semantic Enterprise Architecture

Business Reference Model (BRM)• Lines of Business• Agencies, Customers, Partners

Service Component Reference Model (SRM)• Service Layers, Service Types• Components, Access and Delivery Channels

Technical Reference Model (TRM)• Service Component Interfaces, Interoperability• Technologies, Recommendations

Data Reference Model (DRM)• Business-focused data standardization • Cross-Agency Information exchanges

Performance Reference Model (PRM)

• Government-wide Performance Measures & Outcomes• Line of Business-Specific Performance Measures & Outcomes

Federal Enterprise Architecture (FEA)

Busin

ess-D

riven A

ppro

ach

(Citize

n-C

ente

red Fo

cus)

Com

ponent-B

ase

d A

rchite

cture

s


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Semantic Federal Enterprise Architecture (FEA) Capabilities Manager in early 2003

Knowledge About:AgenciesFEAEA300PartnershipsCapabilitiesComponents

Selected Capabilities

Recommended Partnering

Published Capabilities


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Ontology-Based EA Registry: TopSCAPE-EAFEA and DOD extensions

Select either FEA Ontology or Agency-Specific Ontologies

Service specifications with links to more details

Search over all models for concepts

Demonstration at www.topquadrant.com/EAworld/index.htm


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TopSCAPE-EA:Search Example – “Quality”

Search results show FEA path

Demonstration at www.topquadrant.com/EAworld/index.htm


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TopSCAPE-EAExample of DOD extensions to FEA

Agency-specific extensions shown “green”

Hot links to TRM areas


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Mapping Components to the FEA Models - 1

Available elements from merged reference models


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FEA-RMO delivers “Line of Sight”

fea: Mission

fea: intentOf

fea: Agency

brm: provides

brm: SubFunction

fea: hasIntent

brm: hasProcessbrm: Process

brm: usesResource

brm: Resource

brm: hasPerformance

prm: PerformanceMeasure

prm:hasIndicator

prm: GenericMeasurementIndicator

fea: Customer

prm:hasSpecialization

prm: OperationalizedMeasurementIndicato

r

brm: hasCustomer

srm: Service

brm: realizedWith


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A New Way for OMB and Agencies to do Budget Proposals

Pro

po

sed

Bu

sin

ess

Ca

se

Sco

pe V

alid

ate

A

ssess

FEA Reference Models•feedback re. quality

•feedback re. use•feedback re. relevancefor strategic planning,

capital planning,& risk management

Metrics

Recommendations for improvement and

partnering

Ontology-Based Budget

ProposalRepository

Validate Value Proposition

C. Potential for reuse of Technologies and Components

A. Business Area and LOB supported?

B. Number of proposals with same capabilities?

Budget Proposal Assessment

FEA

Onto

logy-B

ase

d Im

port

D. Synergies for partnering

Adapted from: Dr. Michael J. Kurtz, “The Role of Electronic Records Management in Implementing eGovernment: Electronic Records and the Federal Enterprise Architecture”, NARA, 4/15/04

Assessment down from 3 months to 7 weeks

Re-submit period up from 1 week to 6 weeks - allowing time for collaborations to be negotiated


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Semantic IT Governance

TopSCAPE-ITG

Web-Services

Web Services

Active ModelsCC

FEA Models

Government Agencies

Analytics Engine

Web

Web-Services

Business Cases

Partnerships and Projects

OW

L

OW

L

OW

L

OW

LA

naly

tics

Bro

kerCCCC

Rules and Policies

Capabilities and

ComponentsComponent Registries

Forms and Documents

OW

L

IT G

overn

ance

D

ash

board

CC

Deci

sion

Su

pport

Advis

or

Dynam

ic F

orm

s B

roker

CC

Busi

ness

Case

C

onst

ruct

or

CC


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FEA-RMO Lessons

Good natural language Reference Models help ontology development

Modeling Principles and Patterns are key – often evolve iteratively

Modular Architecture benefits concurrent lifecycle management

OWL works and reasoning pays off in generic code

Semantic Applications can be built quickly


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Semantic Technology is “Here and Now”

Semantic Technology can help enterprises that face issues analogous to NASA to: Organize, resource, and manage complex, long-term

programs Develop products, systems, services, infrastructure that

are smarter and more sustainable Better direct and control acquisition of major systems and

capabilities across a lifecycle Integrate, interoperate, orchestrate complex processes

across extended enterprise Enable model-based collaborative environments such as

virtual design centers, decision support environments for acquisitions, distributed operations


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References

Dean Allemang, Irene Polikoff, Ralph Hodgson, Paul Keller, Jason Duley and Paul Chang: “COVE – Collaborative Ontology Visualization and Evolution”, IEEE Aerospace Conference, Montana, 2005 http://www.aeroconf.org/aeroupload/finishedpdf/F1458_2.pdf

TopQuadrant White Paper on FEA-RMO, 2/21/2005 http://www.topquadrant.com/tq_ea_solutions.htm


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Q & A

Ralph Hodgson

E-mail: [email protected]

www.topquadrant.com

(703) 960-1028