Kevin_Gupton - Buffalo Ontology Site

Report
UNCLASSIFIED
Applications of Ontology OWL to:
• Geospatial Feature Data Dictionaries
• Rapid Data Generation: Order of Battle and
Entity Type Data Management
Kevin Gupton
[email protected]
512-835-3679
Modeling & Simulation Information Management Branch
Signal and Information Sciences Laboratory
Applied Research Laboratories
The University of Texas at Austin
1
My involvement
• Participation
• Projects:
– M&S COI Data Management Working Group
– ASW COI Data Management Working Group
– NATO M&S Group (MSG) 085 – C2 &
Simulation Interoperability
– Simulation Interoperability Standards
Organization (SISO)
• Standards Activity Committee
• Military Scenario Definition Language (MSDL)
• Coalition Battle Management Language (CBML)
• Simulation Conceptual Modeling (SCM)
• Architecture-Neutral Data Exchange Model
(ANDEM)
– M&S Coordination Office
– US Army Simulation to C4I
Interoperability (SIMCI) OIPT
– Joint Staff J7 Joint Coalition
Warfighting (formerly JFCOM)
• Coordinated with
– US Army Operational Test Command
– AMSAA
– Global Force Management Data
Initiative (GFM DI)
– US Army PD Tactical Network
Initialization
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UNCLASSIFIED
THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
10F-SIW-068
Mapping Data Models and Data
Dictionaries – Removing the Ambiguity
Kevin Gupton
[email protected]
512-835-3679
Eric Allcorn
[email protected]
Roy Scrudder
[email protected]
512-835-3857
Bruce Carlton
[email protected]
512-835-3120
Modeling & Simulation Information Management Branch
Signal and Information Sciences Laboratory
Applied Research Laboratories
The University of Texas at Austin
3
UNCLASSIFIED
Overview
• Background
– Data dictionaries must be mapped to enable translation and
reuse of datasets and tools based on one data dictionary or
another.
• Problems
– Current mapping processes use English language and
spreadsheets to capture the mappings.
– Too much room for interpretation.
– Difficult to evaluate or compare mapping results.
– No clear path to using mappings in data mediation software.
• Our objective
– Explore and demonstrate the benefits of an ontology-base
approach to data dictionary mapping.
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
We focused on mapping of EDCS and NFDD ...
• EDCS – SEDRIS Environmental Data Coding
Specification
• NFDD – National System for GeospatialIntelligence (NSG) Feature Data Dictionary
• Both are dictionaries of geospatial feature concepts
• Both contain concepts as:
• Features / Classifications
• Attributes
• Enumerations
• Both provide definitions for each Concept,
but little or no taxonomy or relationships
• Both are available as MS Access Databases
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
...But recognized that there are others.
Sub-schemes and implementation schemes
• Some schemes are “based” on a common data dictionary, but semantics have
drifted and diverged for various reasons.
• Some schemes are not based on any common data dictionary.
Environment-related thesauri:
• GEMET – GEneral Multilingual Environmental Thesaurus
• AGROVOC – a thesaurus of agriculture, forestry, fisheries, and other domains
• NALT – National Agriculture Library Thesaurus
Unlike NFDD and EDCS, these are actual thesauri with broader /
narrower relations, preferred and alternate names, definitions, etc.
6
General use knowledge bases:
• DBPedia – a structured extraction of the Wikipedia body of knowledge
• OpenCyc – Open source Cycorp
knowledge base
APPLIEDgeneral
RESEARCH LABORATORIES
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• WordNet – Lexical database
of the English Language
UNCLASSIFIED
AGC Mapping Relations
AGC Relation
AGC Example
Set Theory Relations
Concepts are completely disjoint
(EDCS:Terrain Plain, NFDD:Slope Region)
(EDCS:Complex Outline, NFDD:Facility)
A B 
Concepts overlap completely
(EDCS:Parcel, NFDD:Parcel)
(EDCS:Marine Port, NFDD:Port)
A B
Concepts overlap well
(EDCS:Glacier, NFDD:Glacier)
(EDCS:Opera House, NFDD:Building)
(only 7 of 18 shown here)
A B  
A\ B  
B \ A  
A \ B and B \ A are “small”
Concepts overlap somewhat
(EDCS:Sports Arena, NFDD:Sports Stadium)
A B  
A\ B  
B \ A  
Concept A is a generalization of
Concept B
(EDCS:Harbour, NFDD:Harbour)
(EDCS:Route, NFDD:Ice Route)
A B
Concept A is a slight
generalization of Concept B
(EDCS:Traffic Light, NFDD:Traffic Light)
(EDCS:Astronomical Station, NFDD:Astronomical
Observatory)
A B
Concept A is an aggregate of
Concept B
(EDCS:Airfield, NFDD:Runway)
(NFDD:Tent, EDCS:Camp)
A “has part” B
B “is part of” A
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A \ B is “small”
Set Theory
shows duplicate
relationships
with ambiguous
differences.
UNCLASSIFIED
AGC Mapping Relationships
OWL is built upon set theory, where OWL classes are sets.
AGC Relation
(only 4 of 18 shown here)
Class Diagram Visualization
Concepts are
completely disjoint
A
AGC Relation
(another 3 of 18 shown here)
Class Diagram Visualization
Concept A is a
generalization of
Concept B
B
A
B
A B 
Concepts overlap
completely
Concept A is a slight
generalization of
Concept B
A B
A
A\B
B
“small”
Concepts overlap well
A
A\B
Concepts overlap
somewhat
A
A\B
has part
“small”
B
A B
B\A
Set Theory shows duplicate relationships
with ambiguous differences.
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A
B\A
A B
“small”
Concept A is an
aggregate of Concept B
B
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B
UNCLASSIFIED
“Qualified” Relationships
• A “qualified” relationship is one that hold under some known condition or
criteria.
• Described as one or more attributes having certain values.
• In the examples below, a qualification Q1 on concept B forms
a subconcept BQ1.
Unqualified
Qualified
Clearly all A are in B,
but we don’t know if b in B is in A.
B
B\A
All A are in B, and
we know what subset of B equals A.
B
A=B?
 We can map A to B but not B to A.
Combined qualification examples:
BQ1=A
B\A
 We can map A and B bidirectionally (lossy).
A
B
B
AQ1=A \ B
BQ1=A1
BQ2=A2
BQ3=A3
BQ1=B \ A
AQ 2  A  B  B Q 2
Both AGC and SEDRIS team schemes capture qualified relationships.
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UNCLASSIFIED
Example of “Qualified” Relationship
AGC Relation: “EDCS (Well) and NFDD (Fountain) concepts overlap completely
(qualified)”
EDCS
EDCS: Well
NFDD
EDCS: Well
Q1: well type = ‘Fountain’
equivalent
NFDD: Fountain
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
Integration and Linking of Dictionaries
• Potential outcome: Integration of data dictionary concepts
– More than just mapping
• Semantic alignment across multiple data dictionaries
• Example: NFDD “railway” and EDCS “railway track”
AGC Relation: “NFDD (Railway) is an aggregate of EDCS (Railway track)”
Railway
network
Infrastructure
Aggregate of
Track
EDCS
NFDD
Railway
Railway track
Railway
GEMET
Equivalent
AGROVOC
WordNet
High-speed
railway
Underground
railway
Railroad
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UNCLASSIFIED
Using Mappings in Data Translation
Relation
Class Diagram Visualization
Concepts A and B overlap
somewhat
Unqualified
NO
Some elements map, but we
don’t know which ones!
Some elements map, but we
don’t know which ones!
YES, WHEN APPROPRIATE
YES, WHEN APPROPRIATE
The qualification tells us
which elements map.
The qualification tells us
which elements map.
A
NO
YES, ALWAYS
B
Some elements map, but we
don’t know which ones!
Subset relationship implies
membership in superset A.
YES, WHEN APPROPRIATE
YES, ALWAYS
The qualification tells us
which elements map.
Subset relationship implies
membership in superset A.
A might imply existence of B
B might imply existence of A
B
A\B
B\A
A B
A
B
AQ1=A \ B
BQ1=B \ A
AQ 2  A  B  B Q 2
Concept A is a
generalization of
Concept B
Unqualified
Concept A is a
generalization of
Concept B
Qualified
Concept A is an
aggregate of
Concept B
A
AQ1=B
A\B
A
has part
B
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Mapping B to A?
NO
A
Concepts A and B overlap
somewhat
Qualified
Mapping A to B?
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UNCLASSIFIED
Roadblocks: The same old problems
Garbage In  Garbage Out
• With poor mappings, we get wrong data faster.
• Weak semantics in data dictionaries beget poor
mappings.
• Both EDCS and NFDD Concepts have:
– Short definitions.
– No scoping or context statement.
– No relationships to other Concepts (internal or external) to
capture the intended “world view”.
• Perhaps NFDD and EDCS should be mapped onto
themselves first?
– EDCS includes a partial taxonomy in its definitions, but can
be more precise.
Weak semantics in EDCS and NFDD perpetuate ambiguity.
APPLIED RESEARCH LABORATORIES
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
Rapid Data Generation
RDG
Rapid Data Generation (RDG)
Kevin Gupton
[email protected]
512-835-3679
Modeling & Simulation Information Management Branch
Signal and Information Sciences Laboratory
Applied Research Laboratories
The University of Texas at Austin
14
RDG Background
UNCLASSIFIED
• RDG is a High Level Task (HLT) selected by the DoD M&S Steering
Committee (M&S SC) for funding through the M&S Coordination
Office PE to address M&S Enterprise Data issues
• Mr. Tom Irwin, Joint Staff (J7), and Dr. Amy Henninger, Army, are the
M&S SC co-leads for governance of RDG
• Government PM was Mike Willoughby, JTIEC; replacement TBA
• Performers are JS J7 JCW (MITRE & GDIT), University of Texas
Applied Research Laboratory, Oak Ridge National Laboratory and
others
• Objective: Reduce the resources required to integrate and initiate
data, eliminate or reduce duplicative efforts, and promote data
commonality for M&S activities across the DoD.
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RDG Summary
UNCLASSIFIED
• RDG implements the DoD Net-Centric Data Strategy (NCDS) by
making data
–
–
–
–
Visible – search via SOA services or a user interface
Accessible – access via SOA services
Understandable / Interoperable – described by structural metadata
Trusted – controlled access to data integrated from authoritative data
sources
• RDG implements the DoD Net-Centric Services Strategy (NCSS) by
– making information and functional capabilities available as SOA services
• RDG implements the DoD M&S Enterprise Data Strategy by
– Implementing the NCDS and NCSS for M&S data
– Using the M&S Community of Interest (COI) Data Management Working
Group to gain stakeholder input
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
Rapid Data Generation
5 Year M&S Data Enterprise Investment Strategy
FY 10
FY 11
FY 12
FY 13
FY 14
FY 15
Year of Funding
HLT IC2
GFM JTDS
Red & Blue
Order of Battle
DP
FY09/10
HLT-IC2
Capability
Enterprise Approach
SC Oversight
Metrics
Immediate Progress
Requirements Driven
Geospatial, Atmosphere,
Space, Ocean
DP
DP
DP
DP
Logistics
Command & Control
DP
Common Data Production Environment
D P = Development Planning
OOB
Mid Term
Exam
OOB Final
Exam
Other Capability On/Off-ramps
= SC Decision Points
SC Governance, Community Participation, Cross-Doman Interoperability
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THE UNIVERSITY OF TEXAS AT AUSTIN
L
I
F
E
C
Y
C
L
E
M
A
N
A
G
E
M
E
N
T
RDG M&S CDPE OOB Data Services Conceptual Overview UNCLASSIFIED
(Draft Pre-decisional)
“Non-US” Force
OOB Data Provider
Operational
OOB Data
Providers
Joint Training
Data Services
(JTDS) OBS
Integrated
Gaming System
(IGS)
USN Common
Distributed
Mission Training
Station (CDMTS)
M&S
Catalog
(i.e. GFM DI, JPES/APEX, etc.)
USAF Scenario
Generation
Server (SGS)
18
RDG
M&S CDPE
CDPE Portal
Authentication/ Authorization Service
OOB Discovery Service
OOB Subscription Service
OOB Edit/Build Service
Discovery Metadata Update Service
Data Retrieval Service
US Special
OSD/CAPE
Operations
Joint Data
Command
APPLIED
RESEARCH LABORATORIES Support
THE
UNIVERSITY OF TEXAS AT AUSTIN (JDS)
(USSOCOM)
CDPE
Discovery
Metadata
Catalog
Other M&S OOB
Data Provider,
Integrator, or
Consumer Systems
UNCLASSIFIED
Rapid Data Generation
DATA ISSUES
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UNCLASSIFIED
Discovery, Retrieval, and Understanding
Discovery
• Need to tag data products
with “discovery metadata” to
enable visibility through
search services.
• Specifically, need to tag data
products containing Unit, Task
Organization, and related data
so they are discoverable based
on
– Unit identifiers and names
– Unit types and capabilities
– Major end-item equipment
types
– Mission, Scenario, garrison and
other contexts
Retrieval and Understanding
• Need to support exchange
of data in multiple data
formats, including
incompatible ones.
• Need to define and align
the semantics of the
formats.
• Promote convergence of
formats (or schema
fragments) for Order of
Battle-related data.
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Metadata Types
UNCLASSIFIED
DoD Directive 8320.02, “Data Sharing in a Net-Centric Department of Defense”
Discovery Metadata
[Information about a data asset] that allows data assets to be found using enterprise
search capabilities.
Descriptions about the content and context of the asset, including
author, title, pedigree, source, media type, and more.
Structural Metadata
Information provided about a data asset that describes the internal structure or
representation of a data asset (e.g., database field name, schemas, web service tags).
Schemas, grammars, and structures that data assets conform to.
Semantic Metadata
Information about a data asset that describes or identifies characteristics about that
asset that convey meaning or context (e.g., descriptions, vocabularies, taxonomies).
The definitions, references, and models that define the meaning of data
assets to capture intent and preclude misinterpretation. Typically tightly
related to the Structural Metadata.
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UNCLASSIFIED
Relationship of Discovery Metadata to OOB Data
“Metacard” for
Data Asset
OOB
Data Asset
Format / XML
Schema
• Stored in a metadata repository
• Shared to catalogs for search and discovery
• Conforms to either
• DDMS
• MSC-DMS
• Augmented with
• Ucore/C2 Core content for discovery
• RDG extensions for OOB discovery
• Stored in a data repository
• Tagged with a metacard
• Conforms to some structure
metadata (format or structure).
• Stored in the DoD Metadata Registry
(MDR)
• Tagged with a metacard
• Conforms to some structure metadata
(format or structure).
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UNCLASSIFIED
What is meant by “Order of Battle?”
Perspective:
•
Authorized
•
On-Hand
•
Planned
•
Anticipated
•
Reported
•
Scenario
•
Organic / garrison
•
Task Organized (OPORD / FRAGO)
Scope / resolution:
•
Operational vs. Systems Architecture
•
Aggregated vs. entity-level
•
Contains network?
•
Contains readiness and holdings?
•
Contains locations?
•
Contains plans and orders?
‘SIDES’
UNITS / ORGANIZATIONS
Friendly
Hostile
Neutral
Organic
Assigned
Attached
OPCON
TACON
Direct support
Reinforcing
General support-reinforcing
General Support
PLATFORMS & LIFE FORMS
Validated for purpose:
•
Acquisition
•
Analysis
•
Experimentation
•
Intelligence
•
Planning
•
Training
•
Test & evaluation
Verified for system needs:
•
C4I system initialization
•
C4I network initialization
•
Simulation and instrumentation initialization
23
Nations
Coalition
Civilian
OPFOR
C2 Network
Locations
Logistics
Plans, orders,
control graphics
Entity (unit, platform, and life
form) type definitions
Platform / weapon /
sensor composition
Application-specific details
Symbols, icons,
System
3D models
environment
Agent/Behavior
models
System data
format
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Characteristics and
Performance
P(hit), P(kill),
P(detect), P(classify)
Making OOB Searchable
“Metacard” for
Data Asset
UNCLASSIFIED
Annotated with UCore
content to support
IC/DoD CDR
OpenSearch
Based on either
DDMS
or
MSC-DMS
RDG OOB discovery
metadata extensions
OOB
Data Asset
Units
• Name
• UIC & FMID
• UTC
• Symbol code
• Echelon
• Capabilities
• Force relationships
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Equipment Types
• NSN
• LIN
• FMID
Order of Battle Formats
UNCLASSIFIED
Format
GFM DI XML
UCore
C2 Core
Joint Staff J8 GFM DI
DoD CIO / DISA, ODNI, DOJ, DHS
C2 DSSC / Joint Staff and DoD CIO
MSDL
Simulation Interoperability Standards
Organization (SISO)
OBS XML
Joint Staff J7 JCW
Army LDIF address books PEO C3T
SIMCI/PD TNI XML
PD TNI and Joint Staff J7 JCW
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UNCLASSIFIED
Global Force ManagementProblem Statement
We need Global Force
Management Data
Current Unit Locations
“Event” data
Operational Availability
Total US Inventory
Historical archive
Timely, reliable, and
authoritative
What forces do I have?
Where are the forces today?
What residual capability exists?
How do I manage forces, manpower, &
equipment from acquisition to end of
service?
What happens if…?
GFM DI is the Department-wide enterprise solution that:
1. Enables visibility/accessibility/sharing of entire DoD force structure
2. Allows integration of data across domains and systems
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
GFM DI Task 1: Organization Servers
6 Org Servers on NIPR
mirrored and augmented
in 7 Org Servers on SIPR
Raw
Data
AIR FORCE
ARMY
ARMY
MARINE COPRS
CORPS
NAVY
Intel Community
OSD
OSD
JOINT STAFF
JOINT STAFF
Feeder Systems
Org
Server
Force
Structure
DOD
USA USAF
USN USMC
States
ANG
ARNG
Feeder systems document
authorizations in without
enterprise-wide standards
Data from Org Servers
exposed to the enterprise
via NCES messaging
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(Defense Intel only on SIPR)
THE UNIVERSITY OF TEXAS AT AUSTIN
GFM DI: Document “Authorized” Force Structure
as the Basis for “On-Hand” and “Execution”
GFM DI Task 1
What are you authorized?
Authorization data
Authorized by Law and
organized by the
Components
Org Servers
28
UNCLASSIFIED
GFM DI Next Steps Task 2 -- Service/User Systems
What do you actually
What do you have to
have?
operate with and where is
it?
“On-Hand” data
Property Books &
Personnel Systems
ITAPDB, MCTFS,
MilPDS, etc.
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Execution data
Readiness, Logistics &
Personnel Systems
DRRS, JOPES etc.
GFM DI Next Steps: Using OUIDs as Reference for UNCLASSIFIED
Real Equipment, People, other IDs and Reorganizations
Military Force Tracking
OE
A
OUID
OUID
OE
TK 1
URN, UIC, ...
M1
TK 4
M1
C-2
OUID
M1A2
E-6
SSG
19K3O
ASI: K4
UII
OE
Tank Cdr
Gunner
Equipment
EDIPI
RPUID
SSG Smith
Real Property
Loader
Driver
29
Fort
Hood
Organizations
& AuthorizationsAPPLIED RESEARCH LABORATORIES
Person
THE UNIVERSITY OF TEXAS
AT AUSTIN
OE:
OUID:
UII:
RPUID:
EDIPI:
URN:
UIC:
Organization Element
Organization Unique Identifier
Unique Item Identifier
Real Property Unique Identifier
Electronic Data Interchange
Personal Identifier
Unit Reference Number
Unit Identification Code
Example Format Utilization
UNCLASSIFIED
Other
sources
Other formats or
unstructured
AIR FORCE
ARMY
ARMY
COPRS
MARINE CORPS
NAVY
Intel Community
OSD
OSD
JOINT STAFF
JOINT STAFF
GFM Org Servers
Other consumers and
data integrators
Force
Structure
DOD
GFM DI XML
USA USAF USN USMC
States
ANG
ARNG
Army PD TNI
DPDE
Army CADIE
JTDS OBS
SIMCI / PD TNI XML
OBS XML
Army LDIF, etc.
Simulation
systems
ABCS
JCATS
JDLM
SIMPLE
WARSIM / WIM
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OneSAF
MSDL
IGS
UNCLASSIFIED
has-parts
has-BOIP
ENTITY TYPE DEFINITION AND
PARAMETRIC DATA
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UNCLASSIFIED
What are Entity Type Compositions (ETCs)?
• The “real world” / battle space (C2/Log) objects that
must be accurately and consistently modeled across
different simulations of a federation.
M&S ETC Name : SCOUT HMMWV Armored 50 CAL
DIS Enum: 1-1-225-6-1-21-0
M2 .50 CAL MG
• Entity types are “compositions” of a base platform or
person with associated
– weapon systems,
– sensors, and
– other (simulation-relevant) equipment.
• Examples of ETC names:
– M1A2 Tank
– M1A2 with mine plow
– M998 Cargo HMMWV
– M1114 Armored HMMWV with Mk-19
– Scout HMMWV with 50 CAL MG and LRAS3
– Airborne Soldier with M4 rifle
– Infantry Soldier with SAW
• Could include organization and facility types too.
FBCB2/BFT
M1114 HMMWV Up-Armored Armament Carrier
• Some ETC enumeration schemes:
– SISO DIS enumerations
– National Stock Numbers (NSNs)
– Line Item Numbers (LINs)
– US Army Standard Nomenclature
– JLCCTC MRF enumerations
– OneSAF enumerations
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LRAS3
THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
ETCs in Practice
Every
• LVC M&S federation,
• individual simulation,
• local M&S federation site
can have different definitions and names
for the same “real world” ETC.
If ETC definitions are not aligned across the C2 and
M&S enterprise,
• OOB data is not interoperable or reusable
• C&P and PH/PK parameter data cannot be
published or consumed
…without human-analyst intervention.
ETCs are managed in multiple places
ETCs relate to other data
reference data
instance data
Platform
properties
Force
Management
Logistics /
Readiness
Scenario /
Order of Battle
Weapon
stations
Behavior
Models
ETC
Characteristics and
Performance Data
3D Models
Federation
Object Models
C2 / logistics
Logistics
Databases
TRADOC
SISO EWG
JTDS OBS
Service-level Force
Management
Object Models
ETC
GFM ORG
Servers
3D model
Repositories
PD TNI
Every Simulation Site
Foreign / intel
databases
Weapon / sensor
effects evaluators
APPLIED RESEARCH LABORATORIES
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simulation users
THE UNIVERSITY OF TEXAS AT AUSTIN
AMSAA
OTC
JLVC
ARCIC
JLCCTC
ETCs as OWL Classes
• Realization: ETCs are Classes.
• Use existing rules and tools
– Not just simple enumerations
– ETCs are “sets of like things”,
corresponding to classes in the Web
Ontology Language (OWL)
• OWL has class semantics “built in”
– Subclass, restricted class, identifying
properties and relationships
Equipment
Vehicle
M2A3
Bradley IFV
Aircraft
HMMWV M1114
w/ .50 CAL
– To avoid OWL is to redefine the same
semantics and software that is
available today.
• Easier alignment of
“enumerations” to other data
standards:
–
–
–
–
–
–
MSDL
C-BML
JC3IEDM
C2 Core
RPR FOM
TENA LROMs
• We can now use existing OWL tools
for basic editing of ETC knowledgebases.
F/A-18
AC-130E
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UNCLASSIFIED
THE UNIVERSITY OF TEXAS AT AUSTIN
JC3IEDM and OWL
Organizing ETCs using JC3IEDM-based
object-type taxonomy.
But JC3IEDM has three problems that had to
be resolved first:
1. Dual taxonomies for Object-Item and
Object-Type.
– In OWL, they can be combined.
2. Flattening of class hierarchy using
“category codes” to reduce table count.
– Not a problem in OWL, so we fleshed out the full class
hierarchies.
3. Only supports single-inheritance
– Many of JC3IEDM’s conflicts can now be cleaned up by
reconnecting the multiple inheritances.
– e.g., Fox M93A1 – is it a Vehicle or a CBRN equipment?
APPLIED RESEARCH LABORATORIES
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
UNCLASSIFIED
RDG PLAN FOR OOB MODELS &
FORMATS
APPLIED RESEARCH LABORATORIES
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
RDG Concept for OOB Formats
1.
Support what exists today: Enable exchange of any existing or future
data format.
(in accordance with IC/DoD Content Discovery and Retrieval (CDR) Retrieve
specifications)
2.
Define a common, extensible OOB logical data model (LDM) and format
to be a managed union of existing data requirements.
a. Start with GFM DI XML as a “common core” and extend; align with
UCore and C2 Core efforts
b. Require data providers to support the common OOB format (in
addition to any legacy format, optionally)
3. Leverage Entity Type management efforts
4. Align M&S to C2 and logistics representations and data sharing solutions.
Work to converge solutions, where appropriate.
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Principles of OOB LDM
• Goal is to support GFM DI
XML, OBS XML, MSDL, Army
LDIF, etc. content completely.
• Enable dynamic extensibility
to support future data
exchange requirements
without imposing schema
changes to established CDPE
producers or consumers.
• Recognize that there are more
than one valid way of viewing
and modeling the world:
structures, resolution,
dimensions.
XML
XML
XML
38
OWL
XML
XML
XML
UNCLASSIFIED
• Define foundation for aligning
semantics for disparate
formats, schemas, and data
requirements.
• Enable more automated data
translation, and quantify
lossiness.
• Stop inventing ambiguous,
unnecessary M&S corollaries
to real-world concepts.
– Align to operational semantics:
architectures, data models,
doctrine, vocabularies,
taxonomies, etc.
– Coordinate activities with GFM
DI, UCore, etc.
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UNCLASSIFIED
Creation of OOB LDM for RDG
1. Reverse engineer grammars /
XML formats into OWL.
GFM DI XML
2. Construct modular composed
ontologies
GFM DI
OWL
XSD
MSDL
SIMCI / PD TNI XML OWL
PD TNI
XSD
XSD
XSD
XSD
Other models
OBS XML
OWL
MSDL
OWL
Other formats
OWL
OBS
Drafts
complete
In progress
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THE UNIVERSITY OF TEXAS AT AUSTIN
•
•
•
•
•
UCore / C2 Core
DIS Enums
Logistics sources
C-BML
NFDD / EDCS
UNCLASSIFIED
OOB LDM Elements
Started with GFMIEDM v3.5 …
OBJECT-ITEM-TYPE
OT-ESTABLISHMENT
DETAIL
OBJECT-ITEM-ASSC
ALIAS-TYPE
ESTAB-ALIAS
OI-ALIAS
OBJECT-ITEM
OBJECT-TYPE
OBJECT-ITEM-ADDRESS
FACILITY
FACILITY-TYPE
MATERIEL-TYPE
ORGANISATION-TYPE
PERSON-TYPE
ADDRESS
…
…
ELECTRONIC-ADDRESS
PHYSICAL-ADDRESS
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…
MATERIEL
ORGANISATION
PERSON
…
…
UNCLASSIFIED
OOB LDM Elements
Extended to also support OBS v3 …
OBJECT-ITEM-LOCATION
SCENARIO
OWNING-FEDERATE
LOCATION
OBJECT-ITEM-TYPE
OT-ESTABLISHMENT
LINE
OBJECT-ITEM-ASSC
ALIAS-TYPE
ESTAB-ALIAS
DETAIL
POINT
…
OI-ALIAS
OBJECT-ITEM
OBJECT-TYPE
OBJECT-ITEM-ADDRESS
NETWORK-MEMBER
FACILITY
…
FACILITY-TYPE
ADDRESS
MATERIEL-TYPE
ORGANISATION-TYPE
PERSON-TYPE
MATERIEL
PLATFORM
…
…
ELECTRONIC-ADDRESS
PHYSICAL-ADDRESS
ORGANISATION
PERSON
…
ABCS-COMPONENT
…
SIDE
DIS-CODE
FACTION
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Other models to fold in…
UNCLASSIFIED
• OGRE/JACOB
• MIDB
• TRAC - Paul Works, Lee Lacy and Dean Hartley
are developing an ontology for irregular
warfare
• Army PD Tactical Network Initialization
• Coalition Battle Management Language
APPLIED RESEARCH LABORATORIES
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THE UNIVERSITY OF TEXAS AT AUSTIN
UNCLASSIFIED
QUESTIONS?
19 APRIL 2012
Kevin Gupton
[email protected]
512-835-3679
Modeling & Simulation Information Management Branch
Signal and Information Sciences Laboratory
Applied Research Laboratories
The University of Texas at Austin
APPLIED RESEARCH LABORATORIES
43
THE UNIVERSITY OF TEXAS AT AUSTIN

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