TQ TBS Semantic Web Adoption (aRH-v4)

8/8/2019 TQ TBS Semantic Web Adoption (aRH-v4)

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© Copyright 2010 TopQuadrant Inc. 1

Semantic Web TechnologyTopBraid Suite and Solutions

Demos andCustomer Case Studies

TopBraid Suite™ Supporting the Complete Semantic Application Lifecycle

October 27, 2010

Ralph Hodgson

CTO, TopQuadrant




Agenda

Introductions

Brief Brief on TopQuadrant Semantic Technology 101

Ontology 101

TopBraid Suite Demos

Composer

Live

Ensemble

TopBraid Solutions

Enterprise Vocabulary Net

Metadata Workbench

Putting Ontologies to Work

Business Value Areas

Customer Stories

Closing Remarks






A Brief Brief on TopQuadrant

Formed in 2001, TopQuadrant is a well-established tools, products, solutions,

training, ontology development and consulting company with more than 100person years experience in Semantic Web Technologies.

Training

TopQuadrant has

trained more people

in Semantic Web

Technologies than

any other company

in the world.

Tools

TopQuadrant has the leading

RDF, OWL and SPARQL tool

suite that integrates with

ORACLE-11G.

Platforms andSolutions

TopQuadrant has a

semantic platform, a

rapid application

builder, and solutions

for vocabulary and

metadata management

Consulting

TopQuadrant has consultants

that have worked, both in TQ

and in previous companies

(notably IBM), on many

consulting projects. At TQ we

do: Jumpstarts, Competency

Development, Solution

Envisioning Workshops,

Ontology Modeling, Semantic

Application Development

TQ and Oracle




TQ team has authored 4 books on Semantic Technology

TopQuadrant Product Development Team Leadership

Holger Knublauch (Protégé OWL creator)

Jeremy Carroll (JENA Architect)

Chief Scientist and Trainer

Dean Allemang (Co-author Semantic Web for the Working Ontologist)

Semantic Web Thought Leadership

http://www.elsevierdirect.com/product.jsp?isbn=9780123735560

http://www.amazon.com/exec/obidos/tg/detail/-/0321205766/qid=1122653418/sr=8-1/ref=pd_bbs_1/104-2961096-8219145




What is Semantic Technology


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What is semantic technology?

7

“Semantic technology (software) allows the meaning

of and associations between information to be known

and processed at execution time.

For a semantic technology to be truly at work within a

system, there must be a knowledge model of some

part of the world (an active ontology) that is used by

one or more applications at execution time.”

-- TopQuadrant



Semantic Web Layer Cake



S

P

AR

Q

L

Query Language

Semantic Web Layer Cake – Take 2

XML Structures

RDF Relationships A Bhas

Subsystem

RDFS A is a CPUClasses

OWLMore ModelFormalism +Reasoning

CPU1 CPU2

Rules

+ Proof

+ Trust

S



How does Semantic Technologywork?

Ontologies can be merged without the

need for programs or data migration. In

contrast to databases, they can be

evolved readily without affecting

existing queries and programs.

LMCO GT&Lhas Business

Area

Semantic Technology represents information in a neutral form called Triples that consist

of a Subject, a Predicate and an Object. Each of these can have properties that infer newinformation. Each has a unique identity, enabling merging of different datasets.

Subject Predicate Object

GT&L C&TGOhas Line of

Business CS OneSAFhas SIM

C&TGO CShas

Capability

CS JSAFhas SIM

CS CultureSIMhas SIM

ref: http://www.lockheedmartin.com/gtl/GT&L – Global Training & LogisticsC&TGO – Combat & Tactical Ground Operations

CS - Constructive Simulation

+

+

+

CS WARSIMhas SIM

Subject Predicate Object

t t



nto og es are e an un e ot ertraditional technologies

Like databases ontologies are used by applications at run time

(queried and reasoned over)Unlike databases, relationships are first-class constructs

Like object models ontologies describe classes and attributes

(properties)

Unlike object models, ontologies are set-based

Like business rules they encode rules

Unlike business rules, ontologies organize rules using axioms

Like XML schemas they are native to the web (and are in fact

serialized in XML)

Unlike XML schemas, ontologies are graphs not trees and used

for reasoning

11

H d S ti T h l l t t



How does Semantic Technology relate toRelational Technology?

Subject

Predicate

Object

Adapted from a slide by Dean Allemang

From Tables to

Linked Enterprise

Data

Think “Outside of the Table”



Mapping the Terrain of Information

Adapted from: Joel A. Gladding, “Data Maturity Model”, SAIC Lead, Defense Intelligence Data Architecture, Presentation April 2005

Level Maturity Model Types ExpressivityMetadata

TypesTechnology

5 OptimizedSemantics

Ontology Constraints, Rules, InferredRelationships

Multi-layerMarkup

SemanticTechnology

4

Managed

Relationships

Ontology,

Thesauri,

Taxonomies

Static Relationships,

Controlled Vocabularies,

XML/RDF/OWL Naming &

Design Rules, Naming &

Identifier Rules

Attributes,

Semantic Tags,

Entity &

Relationship

Markup,

Semantic

Technology

Proprietary

3

Defined

Content

Thesauri,Taxonomies, XSPand XMLSchemas,

Grammars:ASN.1, EBNF

Containers, Elements and

Attributes, Controlled

Vocabularies, Grammar

Formalisms

Entity Markup,

Attributed Code-

lists, Tags

Filtering,

Clustering.

Categorization

Proprietary

2

Repeatable

Structure

Thesauri, XML

Schemas

& DTDs

Hierarchies, Containers,

Elements and Attributes

Entity Markup,

Attributes, Tags

Proprietary Tools,

Database Tools

1Chaotic Text Documents

& Database

Records

Varies Varies Search Tools



What can go wrong with Data

Data Type

Labeling

Aggregation:

-Structure

-Cardinality

Generalization

Value Representation

Impedance Mismatch

Naming

Scaling and Unit

Confounding

Domain

Integrity

Different primitive or abstract types for same information

Synonyms/antonyms have different text labels

Different conceptions about the relationships among conceptsin similar data sets. Collections or constraints have beenmodeled differently for same information

Different abstractions are used to model same domain

Different choices are made about what concepts are madeexplicit

Fundamentally different data representations are used

Synonyms/antonyms exist in same/similar concept instancevalues

Different units of measures with incompatible scales

Similar concepts with different definitions

Fundamental incompatibilities in underlying domains

Disparity among the integrity constraints

F K Id b t



Four Key Ideas aboutSemantic Web Technologies

1. Canonical data Subject-Predicate-Object Triples

2. Identifiers Composition Construct for Aggregations

3. Schemas are also expressed in Triples and can be

queried using same query language – SPARQL

4. Evolvability – schemas, vocabs and datasets can

readily evolve

RDF/OWL Key Idea # 1



RDF/OWL Key Idea # 1 –

“Think Triples”: Subject Predicate Object

Shahram Aziz Terroristrdf:type

Abdi Mekhamdi

sameAs

Subject ObjectPredicate

Shahram Aziz

Shahram Aziz

hasSubtribe

Daulatzai

Abdi Mekhamdi

associateOf

Mustafa Hussein

RDF/OWL K Id # 2



RDF/OWL Key Idea # 2 –

Identifiers not Names (“Everything has a URI”)

Statements in different models but same URIs means more

information about the same subject – in this case “Shahram Aziz”

sameAsShahram Aziz

Shahram Aziz

hasSubtribe

Daulatzai

Abdi Mekhamdi

RDF/OWL K Id #3 S h T i l



RDF/OWL Key Idea #3: Schema uses Triples -queried with same Language as the models

SELECT *WHERE {

?class rdfs:subClassOf* terrorism:Person .

?class rdfs:subClassOf ?restriction .

?restriction a owl:Restriction .

?restriction owl:onProperty ?property .

?property rdfs:label ?propertyLabel . }

Find all properties associated with all subclasses of ‘terrorism:Person’



What can you do with Semantic Technology?

Represent and Aggregate Knowledge

Infer and Discover New Knowledge

Make more informed Decisions

Context-Based Information Provisioning

Integrate Disparate Databases

Implement Semantic Recommenders

Mediate information and capabilities in SOA-Based

Solutions



Mapping Capabilities

Ontology DrivenInformation Retriever

Semantic Multi-Faceted Search

Concept-Based Search

Semantic-Enabled Competency Network

SemanticData

Integrator

Product Design Assistant

Semantic SOA

Information Aggregator

Semantic Data Registry

ApplicationIntegrator

Recommender

GenerativeDocumentation

Context-Aware Retriever

NavigationalSearch

AnswerEngine

Connectionand Pattern

Explorer

Semantic-EnabledCommunities

Semantic Workplace Semantic Mediation

FacetedSearch

WEB 2.0Mashups

I n f o r m a l

Human

F o r m

a l

Machine

E x p r e s s i v i t y



Semantic Model-driven Applications

Rapid Deployment of Agile, Evolvable Applications“Designed for users … Built for change”

By combining capabilities for: – Semantic data integration

– Semantic model-driven applicationassembly, configuration and use

– Configurable components for dynamic

presentation and interaction

RDBs

RDBs

EXCEL

RSS

XML

S

CustomApplication

http://h41201.www4.hp.com/tradein/uk/en/promotions/ProLiant-Server/

http://h41201.www4.hp.com/tradein/uk/en/promotions/ProLiant-Server/



Putting Ontologies to Work

W e a

k

Ontologies as

Design- Artifacts

S t r o n g

Ontologies as

Run-Time Artifacts

Read/WriteModel-Driven

Applications

Reference Models Transformation to

other artifacts

Conceptual Models

for analysis, design and

communication

Linked-Open Data Apps (now)

graph traversals and

aggregations with or

without schemas

Model-Driven Generative

Applications configuration of and/or

transformation to

software modules Read-Only Model-

Driven Applications

Query-Response

Databasesw ith / without rules

and/or inferencing

R o l e o f M

o d e l s a n d S c h e m

a s

Controlled Vocabularies

used by applications

1

8

2

3

4

6

7

10

Semantic

Mediated

Applications

9

Orchestrated

ApplicationsDataflow/Workflow/

Process

5



Ontology-Driven Service-Oriented Architecture

1995 1998 2001 2004 2007 2010

SOAPCORBA/COMJINI

WSDL WS-* REST The “CLOUD”

OWL-S Linked Open Data

WSDL

WS-*

Ontology-Driven SOATransport

Messaging

Description

Composable

Service

Assurances

Service

Composition

HTTP, TLS, MSMQ, SMTP

XML, SOAP, WS-Addressing, WS-Routing

XSD, WSDL, UDDI, WS-Policy

Security

WS-Security

WS-Trust

WS-SecureConversation

WS-Federation

BPEL4WS

WS-ReliableMessaging

Transactions

WS-CoordinationWS-AtomicTransaction

WS-BusinessActivity

ERP PLMCRM DataWarehouse

Unstructured Data(e.g.documents)

Dataon theWeb

Semantic SOA

Adaptor Adaptor Adaptor Adaptor Adaptor

W S

Query BuilderResults Processor

Semantic Cache

Query Dispatcher

Query OrchestratorRules Engine

Triple Store

Adaptor

Web Services Controller

W S

W S

W S

W S

W S

W S

W S

W S

W S

Mapper

Acquirer






Semantic Technology Adoption

Commitment to Semantic Technology

K n o

w l e d g e / E x p

e r i e n c e

Curiosity

Skepticism

Adoption

Enthusiasm

Advocacy

Awareness

Appreciation

Preference

2006

2008

2010

2002 Ignorance

2004




TopBraid Suite ™




TopBraid Suite Components

Ontology

development

Oracle Relational

DBs

Oracle 11g RDF

triple store

Oracle 11g Native

RDFS/OWL/RulesInferencing

Domain-specific

applications (Finance,

Life Sciences,Engineering, Health Care,

National Security, …




TopBraid Suite




TopBraid Suite Architecture Stack

TopBraid Suite seamlessly fits intoexisting IT environments by

providing customer-specific

approaches to complete semantic

application development.




TopBraid Composer

An RDF/OWL IDE with many powerful editing, importing,exporting, inferencing and integration capabilities.

TBS - an Integrated Standards-based




TBS - an Integrated Standards-basedPlatform (1)

Personal server on Composer Maestro Edition (TBC-ME)

Test TopBraid Live applications on localhost open TBC-ME, then open a browser to: http://localhost:8083/tbl

View any model in TBC-ME

workspace with Default

Application

TBS - an Integrated Standards-based




TBS - an Integrated Standards-basedPlatform (2)

Develop model, scripts, etc. in Composer

deploy project to TopBraid Live server

T B id Li S




TopBraid Live Servers

TopBraid Live Platform

RDF/RDFS/OWL, SPARQL, import/export, SPIN, SPARQLMotion

TopBraid Composer

Maestro EditionTopBraid Live

Tomcat, WebLogicEclipse Jetty

TopBraid Live

Enterprise Server

Web server

TopBraid Live

Personal Server

localhost

T B id Li S (TBL)




TopBraid Live Server (TBL)

TBL S C fi i




TBL Server Configuration

TBS A C t l Edit




TBS Access Control Editor

SPARQL Rules




SPARQL Rules(SPIN - SPARQL Inferencing Notation)

First object-oriented Semantic Web language that applies business

logic to data.

SPARQL Rules Motivation: Need to express business rules in models not code

Object-Oriented modeling is turning out to be more relevant than Logic-Oriented (like previous Semantic Web Modeling Languages)

Based on SPARQL standard for expressing Semantic Web models

Key Features:

SPIN Inference Rules

SPIN Constructors

SPIN Constraints

SPIN Meta-modeling

SPIN Standard Modules Library

SPARQL R l (SPIN) O i




SPARQL Rules (SPIN) Overview

SPARQL Rules (SPIN) builds on W3C’s SPARQL query language

define data processing rules, constraints, queries, etc.

Defined SPIN

function

Constraint violation

warning rule

Rule for creating

new instances

Inference

rule

Parameterized

template for rules

SPARQL R l B fit




SPARQL Rules Benefits

Benefits:

Make business rule definition available to a larger range of industrial users

Express rules and constraints with a richer language thanpossible with OWL or other rule languages

Takes advantage of SPARQL development in support of model-driven applications

Contributes to advancement of Semantic Web industry throughW3C cooperation

Availability:

SPIN API available as an open source Java API http://spinrdf.org Distributed under dual license by TopQuadrant

Find out More at: http://www.sparqlrules.org

REST W b S i

http://www.sparqlrules.org/

http://www.sparqlrules.org/




REST Web Services

Web service calls to SPARQLMotion scripts URL embedded in any HTML or application

http://localhost:8083/tbl/actions?action=sparqlmotion&id=FindByCallingCode&callingCode=61

Personal Live server URL Function name parameters

Get parameters

Get linked data

from DBPedia

Create text from

JSP template

return text

Access to all

SPARQLMotion

features

Import Data

T B id S l ti




TopBraid Solutions

Enterprise

VocabularyManagement

Flexible solutions for managing business

vocabularies in support of content delivery,search, navigation, data integration anddisambiguation of terms

Semantic-XML

MessageBuilderWorkbench

Enables XML-based data exchanges thatare specific to the local context whileremaining compliant with industry andenterprise standards

Data

Integration

Federated access to disparate information

sources

EnterpriseArchitecture

Solutions for IT governance andmanagement



Screenshot Tour

TopBraid EVN can manage unlimited




TopBraid EVN can manage unlimitednumber of interconnected vocabularies

Vocabularies can import each other, with each




Vocabularies can import each other, with eachvocabulary having its own set of permissions.

Viewing and editing applications with a




Viewing and editing applications with amulti-page user interface

Concept Hierarchy page shows a tree of concepts, the detailed view for each

selected concept and a list of all properties.

A lookup field at the top right makes it easy to quickly find




p p g y q yconcepts using their preferred, alternative or local language labels.

For ease of editing EVN supports not only auto-complete, but also




g pp y p ,drag and drop between components and even across pages

EVN provides a highly granular history and keeps an




p g y g y paudit trail of all changes

TopBraid EVN supports rich text editing and wiki-style text linking




p pp g y gof concepts. URLs to external web sites can also be included.

EVN helps to ensure information consistency and




p yenforce best practices through rules

SKOS rules are already

provided for organizations

wanting to use them (for

example, alternative label

can not be the same as

preferred label). Any number

of organization and content

specific rules can be easily

added.

Convenient edit operations to create multiple concepts at once;merge and clone concepts and trees; find replace and remove




merge and clone concepts and trees; find, replace and removeproperty values for groups of concepts

Concept Types and Lists page shows an overview of all types(classes) and their properties as well as a list of concepts




(classes) and their properties as well as a list of concepts„belonging‟ to each type

New types can be added and information about




yptypes can be readily changed.

An unlimited number of new properties can be




p pcreated for each type

Powerful search forms on the Search Page make it easy to findconcepts of interest based on their property values Users can




concepts of interest based on their property values. Users canselect which values to show as columns in the results report

Users can search using direct conditions on concepts




as well as conditions on associated concepts

For example, one can find all concepts that have ‘has related’ relationship to

concepts with ‘has broader’ relationship equal to “Europe”.

Search results (and any list displays) can be copied directly into




Excel to create custom reports and exports

All recently browsed items are remembered




and can be quickly revisited

Convenient reports show history of all changes




Convenient reports show history of all changes

On the Graph View page users can navigate




through content graphically

Graph visualization can also be used to edit




and to formulate queries

Users can request their co-workers to review any vocabulary itemby providing a direct link that will conveniently open the




by providing a direct link that will conveniently open theapplication with the right item already displayed

Any item managed by TopBraid EVN can have associatedcomments. The time-stamp of the entry and the user name are




co e ts e t e sta p o t e e t y a d t e use a e a eautomatically captured for all comments

Vocabularies can have multiple simultaneousi ki i




versions or working copies

Working copies enable a review and

approval process before publishingchanges. They also make it possible for

different teams to work in parallel.

Working copies have their own set ofi i




permissions

Changes made to a working copy are

‘sandboxed’ and will not be applied to a

vocabulary until they are published by auser who has the right to change the

vocabulary.

When work is done in parallel, EVN users can see if theh d i th ki i ff t th i k




changes made in other working copies affect their work

To support the review and approval process, EVN offersa variety of reports such as a Comparison Report




a variety of reports, such as a Comparison Report

EVN offers a number of reports includingh t ti ti




graph statistics

All content can be exported in RDF/XML andSpreadsheet for ats




Spreadsheet formats




TopBraid Demos



© Copyright 2010 TopQuadrant Inc. Slide 71

DEMO 1: Terrorist Network Explorer

Creating the DEMO Terrorist Database Content




TopBraid

Importers

g

South Asia

Terrorist Portal

Tracking The

Threat

Militant Ideology

Atlas

Dept. of State

Wanted List

Wikipedia

United Nations(consolidated list)

Minorities at Risk

1

2

3

4

5

6

7

8

Global Terrorist

Database

OWL provides

the 360o view SPARQL Rules

Conversion to

OWL Graphs

reat ng t e error st etworO t l M d l




Ontology Models

Ontology Architecture of the Ontology-Based Terrorist Network Workbench




Based Terrorist Network Workbench

Converting Militant Ideology Atlas to RDF/OWL




g gy

<Record name="Abu Qatada al-Filistini" born="1960">

<Alias>Umar Mahmud Abu Umar</Alias>

<CountryOfOrigin>Palestine</CountryOfOrigin>

<Background> Born in Bethlehem when this was still Jordanian territory,

so is a Jordanian national. Moved to London 1993, and lived in

Acton, a West London suburb. Has five children, is 6' tall and ca.

127kg. He is alleged to be a member of al-Qaida's Fatwa Committee,

according to testimony by Jamal al-Fadl in Southern District Court of

New York in February, 2001. The Jordanian government is currently

seeking his extradition to face terrorism charges

there.</Background>

<Notes>Was freed in 3.2005 on bail from Belmarsh (UK) after a more

than two-year term of imprisonment there. The Madrid train

bombers attempted to contact him at Belmarsh before they

committed suicide in their apartment. Tapes of his sermons were

found in Muhammad Atta's apartment in Hamburg. Richard Reid and

ZacariasMoussaoui are both said to have sought religious advice

from him.

</Notes>

</Record>

Semantic

XML

SPARQL

Rules

tant eo ogy t as onvers onSPARQL Rules




SPARQL RulesCONSTRUCT {

?suspectUri a terrorism:Suspect .

?suspectUri rdfs:label ?name .

?suspectUri terrorism:bornIn ?born .?suspectUri terrorism:diedOn ?died .

?suspectUri terrorism:briefDescription ?desc .

?suspectUri terrorism:aliasName ?aliasName .

?suspectUri rdfs:comment ?notes . }

WHERE {

?rec a miaf:Record .

?rec miaf:name-Record ?name .LET (?suspectUri := smf:buildURI("http://oegov.org/terrorism/osg/terrorism#{?name}")) .

OPTIONAL { ?rec miaf:born-Record ?born .} .

OPTIONAL { ?rec miaf:died-Record ?died . } .

OPTIONAL {

?rec composite:child ?bg .

?bg a miaf:Background .

LET (?desc := terrorism:getTextValue(?bg)) . } .

OPTIONAL {?rec composite:child ?alias .

?alias a miaf:Alias .

LET (?aliasName := terrorism:getTextValue(?alias)) . } .

OPTIONAL {

?rec composite:child ?notesNode .

?notesNode a miaf:Notes .

LET (?notes := terrorism:getTextValue(?notesNode)) . } }

SPARQLMotion




Export XML

Import from

multiple formats

Extract data for

output XML

transformation

Apply rules, other

data processing

Ingest Data Processing Pipeline




g g p




Adoption Stories

DoD BMA BEA Explorer –a TopBraid Ensemble Demonstrator




a TopBraid Ensemble Demonstrator



NASA NExIOM, QUDT, TCMX and

Information Architecture Ontologies

NASA Constellation Program




Constellation Program DataArchitecture and Interoperabilitythrough the use of OWL Ontologieswith strategies for co-existence withXML and other data formats.

NASA Constellation Program

CxP70160 ANX10InfrastructureSpecification

CxP70160 ANX11Application Programming

Interface Specification

CxP70160 ANX14Policy and Security

Model

Key to Success is an Ontology Architecture –Five Dimensions turned out to be important




Five Dimensions turned out to be important

• Ontologies partitioned by domains, disciplines, organizations, specificity and time;

• Named graphs aggregated using configuration ontologies according to need;

• Three other dimensions also important: aspect, viewpoint and perspective.

Domain

Discipline

Organization

Specificity

Time

Information Architecture



Information architecture (IA) defines a model, processes and services forhow information is represented, governed and used in systems,

applications, databases, documents and activities in order to ensurecompliance to naming and identifier rules, standard data and informationtypes, controlled vocabularies and coding schemes.

Slide 84

NASA Information Architecture

GovernanceProvenance

Names andIdentifiers

Data Types

Information Types

Algorithms&

Equations

E n c o d i n g

R u l e s

N a m i n g

& D e

s i g n R u l e s

M e t a d

a t a

XML

OWL

Models

Operational Roles of an Information Architecture



85

IA serves as a “Rosetta Stone” for finding authoritative information. Instead of

reading documents, specifications of models and schemas can be queried by both

software and people. For example, a Sensor and its parameters is named and

encoded differently across systems and applications.

XMLRegistry

Database

MetadataRegistry

UDDIRegistry

CASE Registry

S/W ComponentRegistry

Name andIdentifier Registry

Dataelement

Telemetry andCommanddictionary

ReconRegistry

DocumentManagement

System

Ops Nom,CxID

TableColumn

Packet ID

Vehicleconfig

XMLtag

S/Wattribute

Businessobject In files

Businessspecification

DSILRegistry

Sim name

Vehicle SensorName +

Parameters

IA resolves the meanings of all Sensor informationobjects irrespective of their locality.



objects irrespective of their locality.

Slide 86

compliant withName and

Identifier Rules

is an Information Ty pe

is a Model

conformant withEncoding Rules

Sensor and itsParameters

Information ArchitectureReference Model

IA and Ontologies, Time Series Array Example:Mass Properties



Mass Properties

Slide 87

is a Data Type

ModelResources

Ontology Modelfor Time Series

Arrayis a

Mass PropertiesInstance of a Time

Series Array

is an Information Type

Information ArchitectureReference Model



TMC - Paradigms



Slide 89

UnmannedManned

MissionControl

LaunchControl

WSC

NASA Vehicle

TDRS

ESA Vehicle

AutonomousFixed Re-configurable ProgrammableDynamic

TMC - a Choice of Paradigms



Slide 90

AutonomousFixed Re-configurable Programmable

T&C is staticwith allmeasurements,

messages andcommands pre-configured atdesign time

T&C can bechanged foreach mission

and/or flightbefore launch

T&C can bechanged duringeach flight by

uploadingcommands andtelemetry packetdefinitions

T&C systems aresmart and canbe instructed to

do eventmonitoring,tasks, journalingand other jobson-demand.

Space systemsare capable ofmaking their

own decisionsusing agent-basedtechnologies

Dynamic

SHUTTLE

APOLLO

ISS

ARES, ORION

MARS Rovers

MEX, VEX, Rosetta

Unmanned

Future Space Systems

MSL

Manned

Proba-2, Aeolus

Space Interoperability Scenario 1:Proximity Operations Between 2 Space Vehicles



y p p

91

Local DataBuffer

Local DataBuffer

DataProcessing

Flight OPS

FIFOFIFO

Space Interoperability Framework

ProcessedFlight DataRepository

Processed FlightData Repository

CPUCPU

DataProcessing

Command 2:Turn on ProximityOperations telemetryPackets

Command 1:

Can we interoperateat level 4?

Reply 2: Proximity Operations Telemetry active

1

2

2

3

Reply 1: Yes

ProximityOperationsTelemetry

1

NASA TCMX: Ontology-BasedTelemetry, Commands and Messaging



92

e e et y, Co a ds a d essag g

Types Instances Qualification Packaging Workflow

Ontology and

SchemaComponents

TCM-T

Telemetry,Command,etc. types

TCM-I

Telemetry,Packets,

and

Commands

TCM-Q

Phase,Access,

Criticality,Sampling

TCM-P

Packaging

references controlled terms and code-lists

NASA Controlled Vocabularies

QUDT – Quantities, Units, Dimensions and Data TypesMission PhasesAccess GroupsHardware Criticality

…

System, Vehicle, or Subsystem

defines types for




qualified by

Provide criteriafor packaging

selected for packaging


TCMX – Generating XML Schemas and Vocabulariesfrom OWL Models for Space Systems Interoperability



from OWL Models for Space Systems Interoperability

Slide 93

Modular OWL

SpecificationModels

XML SchemaPlus(XSP)

XML Schema

XMLVocabularies

Modular TCMx, QUDTand System Ontologiesare transformed throughannotations to anintermediate languageXSP for controlling theXML Schema Generation

XMLSchemaPlus (XSP) is anXML Dialect for specifying howan XML Schema should look.An XSLT script ensurescompliance with XML Namingand Design Rules

XML Schemas usecontrolled vocabularies toensure semanticconsistency of referencedconcepts such as units of measure, quantities andcodelists.

SPARQL Rules

XSLT

TopBraid Composer Altova XMLSpy

References: QUDT – http://www.qudt.org , XSP – http://www.xspl.us

TCMX Ontology Architecture builds ofSystem SBFI and QUDT Ontologies

http://www.qudt.org/

http://www.xspl.us/

http://www.xspl.us/

http://www.qudt.org/



y g

Telemetry and CommandPackaging

Telemetry Parameter andCommand Instances

Telemetry Parameter andCommand Qualifications

Telemetry Parameter andCommand Types

TCM Foundation

Mission Ontology

System Ontology -Functional Aspect (af)

System Ontology -Structural Aspect (as)

Generating XML Schemas and ControlledVocabularies



GRDDL XSLT Generator

XSLTProcessor

Going from XML to OWL

Ref: XML SchemaPlus – http://www.xspl.us




Enterprise Vocabulary ManagementSolution (EVMS)

Enterprise Vocabulary Management- The Problem




The Conventional Approach Companies aim for a single, centralized master

vocabulary system

The Reality Vocabularies are managed in a disconnected,

distributed manner

Spreadsheets

Individual user desktops

The Result Information navigation becomes disjointed

Poor information quality and faultycommunication

Directly impacts productivity, customer servicequality, revenues and missed competitiveopportunities

TopBraid EVMSEnterprise Vocabulary Management Solution




How it Works Constructs a

‘Dynamic Web of Terminology’

Creates links between terminology element that wereunconnectedable

p y g

Each Vocabulary can have MultipleSimultaneous Versions




Each Item has a Separate Audit Trail fromMultiple Users




p

EVMS at a Large legal research firm




Key Benefits:

A sustainable, shared and distributed vocabulary web Supports connecting and reusing terms across taxonomies

or allow customizability for the different lines of business

Manages change and collaboration

Improves Data Quality, Search, Analysis andInteroperability Taxonomies are used to tag publications, providing added

value over competitors in the ease with which materialscan be accessed

Ultimately impacts productivity, customer servicequality, revenues and competitive opportunities




Semantic-XML Message BuilderWorkbench (SMBW)

Solution Example




TopQuadrant‟s Metadata Workbench Solution

at

The Netherlands Ministry of Justice

Justice Data Exchange covers many Domains of Law




Seamless data exchanges are challenging:

Unique data requirements exist at courts, police, hospitals, border control, motor vehicle,

local and federal offices.

Traffic Violations

Criminal Justice

Child CareProtection

Immigration

Drug Abuse

Litigation

“Living in the XML Ecology”: Challenges




Addressing the "standards dilemma“

Too many exchange mechanisms and

standards

Lack of conformance to XML (and OWL)

Naming and Design Rules

Brittleness in data models:

Often very complex,

Often incomprehensible

Sometimes non-implementable.

X

X

Ontology-Driven Approach to Message Designfor Interoperability




Solution: Ontology-Based Metadata Workbench:Transform Domain Models into UN/CEFACT CCTS compliant representation and allow Business

Analysts to assemble business documents for electronic messages from Component Parts.

UN/CEFACT NDR

Creation of XML Message Schemas




Rich

Ontologies

CCTS Ontologies

Core Component

Overlay

Contexts

Domains

Business

Document

Ontologies

CCTS

MetaModel

CCTS

Document

SPIN Transformation rules

CCTS XML

SchemaPlus

CCTS XML

Schema

XSP

MetaModelXSLT Script

Business

Component

Overlay

“Rich” Ontologies areexpressive models of

domains. These include

LKIF and detailed situations

of law and legal document

and procedures.

CCTS-Compliant XML Schemas

are generated from the XSPDocument

CCTS Document Editor XSP Generation XSD Generation

Users create CCTS documents

from BIEs and Core

Components

Projects

Acronyms

BIE Business Information Entity

CCTS UN/CEFACT Core Component Technical

Specifications

LKIF Legal Knowledge Interchange Format

SPIN SPARQL InferencingNotation

XSLT XSL Transformations (XSLT) Version 2.0

XSP XML SchemaPlus

The Netherlands Ministry of JusticeMetadata Workbench




Example of a User Composing a Business Document from the CCTS Core Vocabulary.

Benefits of the MoJ Solution




Business Benefits Accurate and rapid Information Sharing between Organizations

Agility in response to Legislation Changes

Data Quality is guaranteed

Reduced Costs of Message Schema Development

Technical Benefits Direct and flexible Reuse of Data Components

Full Automation of XML Schema creation

Semantic Consistency is preserved and confirmed

Linked Data / traceability

Version Management

See Enterprise Data Journal Article for more details on this solution:

http://www.enterprisedatajournal.com/article/netherlands-ministry-justice-metadata-workbench-

composing-xml-message-schemas-owl-models.htm

http://www.enterprisedatajournal.com/article/netherlands-ministry-justice-metadata-workbench-composing-xml-message-schemas-owl-models.htm


























Best Practices

nto ogy o e ng:Some Important Guidelines




p Establish common practices: ontology architecture, modelling

patterns, rules for names and identifiers, concept and property

names, and namespaces

provide human-readable names with rdfs:label

use domain and range with care

take care over open and closed world assumptions

Keep ontologies small and modular

Establish an ontology architecture early

Model for reuse – separate instances from classes

Be clear on ontology’s role: specification versus knowledge discovery

Assimilate enterprise knowledge - internal lists, vocabularies, taxonomies Analyze Synthesize Evaluate: Iterate with stakeholders

Agile re-factoring using ontology re-factoring patterns

Test often using sample data

Thank You



Ralph HodgsonE-mail: [email protected]

Twitter: @topquadrant, @ralphtq, @oegovnews

Some References

mailto:[email protected]

http://www.elsevierdirect.com/product.jsp?isbn=9780123735560

http://www.amazon.com/exec/obidos/tg/detail/-/0321205766/qid=1122653418/sr=8-1/ref=pd_bbs_1/104-2961096-8219145

mailto:[email protected]