tq tbs semantic web adoption (arh-v4)
TRANSCRIPT
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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
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© Copyright 2010 TopQuadrant Inc. 2
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
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© Copyright 2010 TopQuadrant Inc. 4
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
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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
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© Copyright 2010 TopQuadrant Inc. 6
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
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Semantic Web Layer Cake
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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
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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
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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
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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”
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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
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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
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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
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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
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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
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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’
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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
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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
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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
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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
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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
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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
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TopBraid Suite ™
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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, …
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TopBraid Suite
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TopBraid Suite Architecture Stack
TopBraid Suite seamlessly fits intoexisting IT environments by
providing customer-specific
approaches to complete semantic
application development.
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TopBraid Composer
An RDF/OWL IDE with many powerful editing, importing,exporting, inferencing and integration capabilities.
TBS - an Integrated Standards-based
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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
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TBS - an Integrated Standards-basedPlatform (2)
Develop model, scripts, etc. in Composer
deploy project to TopBraid Live server
T B id Li S
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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)
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TopBraid Live Server (TBL)
TBL S C fi i
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TBL Server Configuration
TBS A C t l Edit
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TBS Access Control Editor
SPARQL Rules
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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
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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
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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
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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
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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
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Screenshot Tour
TopBraid EVN can manage unlimited
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TopBraid EVN can manage unlimitednumber of interconnected vocabularies
Vocabularies can import each other, with each
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Vocabularies can import each other, with eachvocabulary having its own set of permissions.
Viewing and editing applications with a
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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
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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
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g pp y p ,drag and drop between components and even across pages
EVN provides a highly granular history and keeps an
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p g y g y paudit trail of all changes
TopBraid EVN supports rich text editing and wiki-style text linking
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p pp g y gof concepts. URLs to external web sites can also be included.
EVN helps to ensure information consistency and
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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
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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
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(classes) and their properties as well as a list of concepts„belonging‟ to each type
New types can be added and information about
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yptypes can be readily changed.
An unlimited number of new properties can be
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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
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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
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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
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Excel to create custom reports and exports
All recently browsed items are remembered
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and can be quickly revisited
Convenient reports show history of all changes
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Convenient reports show history of all changes
On the Graph View page users can navigate
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through content graphically
Graph visualization can also be used to edit
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and to formulate queries
Users can request their co-workers to review any vocabulary itemby providing a direct link that will conveniently open the
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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
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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
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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
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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
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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
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a variety of reports, such as a Comparison Report
EVN offers a number of reports includingh t ti ti
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graph statistics
All content can be exported in RDF/XML andSpreadsheet for ats
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Spreadsheet formats
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TopBraid Demos
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© Copyright 2010 TopQuadrant Inc. Slide 71
DEMO 1: Terrorist Network Explorer
Creating the DEMO Terrorist Database Content
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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
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Ontology Models
Ontology Architecture of the Ontology-Based Terrorist Network Workbench
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Based Terrorist Network Workbench
Converting Militant Ideology Atlas to RDF/OWL
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© Copyright 2010 TopQuadrant Inc. Slide 75
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
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© Copyright 2010 TopQuadrant Inc. Slide 76
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
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Export XML
Import from
multiple formats
Extract data for
output XML
transformation
Apply rules, other
data processing
Ingest Data Processing Pipeline
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g g p
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Adoption Stories
DoD BMA BEA Explorer –a TopBraid Ensemble Demonstrator
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a TopBraid Ensemble Demonstrator
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NASA NExIOM, QUDT, TCMX and
Information Architecture Ontologies
NASA Constellation Program
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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
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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
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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
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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.
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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
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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
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TMC - Paradigms
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Slide 89
UnmannedManned
MissionControl
LaunchControl
WSC
NASA Vehicle
TDRS
ESA Vehicle
AutonomousFixed Re-configurable ProgrammableDynamic
TMC - a Choice of Paradigms
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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
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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
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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
references controlled terms and code-lists
references controlled terms and code-lists
references controlled terms and code-lists
qualified by
Provide criteriafor packaging
selected for packaging
references controlled terms and code-lists
TCMX – Generating XML Schemas and Vocabulariesfrom OWL Models for Space Systems Interoperability
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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
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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
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GRDDL XSLT Generator
XSLTProcessor
Going from XML to OWL
Ref: XML SchemaPlus – http://www.xspl.us
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Enterprise Vocabulary ManagementSolution (EVMS)
Enterprise Vocabulary Management- The Problem
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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
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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
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Each Item has a Separate Audit Trail fromMultiple Users
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p
EVMS at a Large legal research firm
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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
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Semantic-XML Message BuilderWorkbench (SMBW)
Solution Example
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TopQuadrant‟s Metadata Workbench Solution
at
The Netherlands Ministry of Justice
Justice Data Exchange covers many Domains of Law
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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
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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
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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
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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
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Example of a User Composing a Business Document from the CCTS Core Vocabulary.
Benefits of the MoJ Solution
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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
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Best Practices
nto ogy o e ng:Some Important Guidelines
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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
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Ralph HodgsonE-mail: [email protected]
Twitter: @topquadrant, @ralphtq, @oegovnews
Some References