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ATL & XMDR Technologies Overview (Developed and Future Pursuits) Benjamin Ashpole bashpole@atl.lmco.com 856-792-9744 Dr. Raj Kant rkant@atl.lmco.com 856-792-9730 http://www.atl.external.lmco.com/projects/ontology/ S Ontrapro Alignment Translator S E4 P S S S S S S S S COACH

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atl xmdr technologies overview developed and future pursuits

ATL & XMDRTechnologies Overview(Developed and Future Pursuits)

Benjamin Ashpole

bashpole@atl.lmco.com

856-792-9744

Dr. Raj Kant

rkant@atl.lmco.com

856-792-9730

http://www.atl.external.lmco.com/projects/ontology/

introduction

S

Ontrapro

Alignment

Translator

S

E4

P

S

S

S

S

S

S

S

S

COACH

Introduction
  • ATL Overview
  • Technology Topics
    • Ontology Alignment prototype
    • Software Agents technology
    • COgnitive Algorithm Composition Handler (COACH) concept
      • Dynamic and Static Application Analysis
      • Service-2-Service matchmaking
      • Explanation generation
      • Service Navigation & Execution
      • Security, Authentication
    • Evaluation
      • I3Con (2004)
      • EON (2004)
      • IC2, STS, MS2 (planned for 2005)
  • ATL and XMDR: Goals
advanced technology laboratories overview

Advanced Technology Laboratories Overview

Jim Marsh, Director

(856) 792-9820

jmarsh@atl.lmco.com

advanced technology laboratories converting research into solutions
Our mission …

Solve world class information technology problems

Provide a consistent stream of technology discriminators for military applications

Our formula …

Advanced technology

Innovation in advanced computing and intelligent software

Exploitation and hardening of emerging technologies

Domain expertise

Path to a product

Integrated solutions with quantified payoff

Proven technology transition

… leads to paradigm-changing payoffs

Advanced Technology Laboratories… converting research into solutions

Add FCS Picture?

advanced technology laboratories
Established in 1929

Key location: Cherry Hill, NJ

88K sq. ft.

Multiple labs (up to TS/SCI)

Core capability: Advanced Information Technology

Intelligent systems

Information architectures

Embedded systems

Wireless communications

Complex system simulation/analysis

Advanced Technology Laboratories

Lockheed Martin

26%

Technologists - 78%

DARPA

37%

• PhD 17%

• MS 49%

• BS 34%

Other

15%

Classified 8%

Gov. Labs

25%

Other 4%

Management

7%

Full Time Employees = 148 as of 10/4/04

Non-ATL/Interns/Visiting Researchers = 14

Total = 162

2003 Customers

emerging challenges require compelling technology
Emerging Challenges Require Compelling Technology

Business Areas

  • Joint/Coalition
  • Operations
  • Rapid Response
  • Info Superiority

Intelligent Systems

  • Network Centric Operations
  • Autonomy and Collaboration
  • Situation Understanding
  • Decision Support
  • National Missile
  • Defense
  • Time Critical Strike

Adaptive Information Systems

  • Dynamic Info. Integration
  • Information Extraction/Exploitation
  • Cognitive Computing
  • Network Mission Assurance
  • Force Multipliers
  • Collaborative Auton. Vehicles
  • Human Augment.

Netted, Embedded & Complex

Systems

  • Advanced Networking
  • Wireless Communications
  • Adv Signal Proc & Embedded Proc.
  • Complex Systems
  • Homeland Security
  • Anti-Terrorism
  • IW
  • NBC
current darpa programs and transition targets
Current DARPA Programs andTransition Targets

Transition, System Emphasis

R&D Emphasis

Technology Focus

Human Machine Interaction

Situation Understanding

Plan Understanding & Monitoring

Autonomy & Teaming

Information Protection

Agent-Based Systems

Composable Simulation

Network Centric Enablers

IPTO

Aug Cog

ASSIST

COORDI-NATORs

RAP Teams

CRABS

FAST C2AP

FM-UAO

PCA, ACIP

NA3TIVE

SAPIENT

ATO

FTN

DTN

XG-Comms

Connection-less Nets

MNM

TTO

UCAR

UCAR

UCAR

UCAR

UCAR

MDC2

Gov. Labs

ONR, NWDC,

Marines

Army AATD,

CECOM, ARL

ONR, AFRL,

AATD

Classified

NWDC, JFCOM,

JL ACTD

JTL ACTD

Various

(AFRL, NWDC,

CECOM, etc.)

LM BU

SI Owego (UCAR), MS2 (HAIL)

SI Owego (UCAR, HSKT, AMUST-D)

SI Owego (UCAR)

ADP Ft. Worth (AO FNC, Autonomy FNC )

MS2 (DD(X), DW, LCS Assured RT)

SI Owego (UCAR)

IXO

DTT

JAGUAR

HURT

PCES

ARMS

Expanding CRAD Base Across DARPA & Government Labs LM

Ongoing

Bid

slide8

Technology Topics

ATL R&D Interests

slide9

Ontology Mapping

Technology Topics

ontology translation protocol ontrapro ontology mapping alignment

A=Z

B=Y

C

D=X

E=W

F

G

H=U

I=T

K

L

M=V

ONtology TRAnslation PROtocol (Ontrapro) – Ontology Mapping & Alignment
  • XMDR Use Cases: Research challenges
    • Semantic Normalization, Disambiguation, and Harmonization
      • Multiple entries within the same taxonomy
      • Similar but significant differences in the entries of two ontologies
    • Mapping and Interrelationships
      • Structure alignment of two different ontologies
  • ATL’s ONTRAPRO prototype addresses these challenges

Data Description(Schema or Ontology)

Ontrapro

Data

Data

example wine ontologies term dissimilarities largely similar but not exactly same

Vinos

Wine

VinosRojos

VinonBlancos

RedWine

WhiteWine

BurdeosRojo

Tempranillo

Chablis

CheninBlanc

RedBordeaux

Tempranillo

Chablis

CheninBlanc

MezclaDeCabernet

Dolcetto

SauvignonBlanc

PinotNior

CabernetMerlot

Dolcetto

SauvignonBlanc

PinotNior

BorgonaRoja

RojoItaliano

Semillon

Muscat

RedBurgundy

ItalianRed

Semillon

Muscat

Chianti

Berbera

WhiteBordeaux

Sake

Chianti

Berbera

WhiteBordeaux

Sake

PetiteSirah

Sangiovese

Chardonnay

PinotGris

PetiteSirah

Sangiovese

Chardonnay

PinotGris

BlancoItaliano

PinotNior

Riesling

Zinfandel

ItialianWhite

PinotNior

Riesling

Zinfandel

BorgonaBlanca

CabernetSauvignon

Nebbiolo

Gewurztaminer

WhiteBurgundy

CabernetSauvignon

Nebbiolo

Gewurztaminer

Syrah

Syrah

Merlot

Merlot

Technology Topics

Example: Wine OntologiesTerm Dissimilarities(largely similar but not exactly same)
slide12

Vinos

Wine

VinosRojos

VinonBlancos

RedWine

WhiteWine

BurdeosRojo

Tempranillo

Chablis

CheninBlanc

RedBordeaux

Tempranillo

Chablis

CheninBlanc

MezclaDeCabernet

Dolcetto

SauvignonBlanc

PinotNior

CabernetMerlot

Dolcetto

SauvignonBlanc

PinotNior

BorgonaRoja

RojoItaliano

Semillon

Muscat

RedBurgundy

ItalianRed

Semillon

Muscat

Chianti

Berbera

WhiteBordeaux

Sake

Chianti

Berbera

WhiteBordeaux

Sake

PetiteSirah

Sangiovese

Chardonnay

PinotGris

PetiteSirah

Sangiovese

Chardonnay

PinotGris

PinotNior

BlancoItaliano

Riesling

Zinfandel

PinotNior

ItialianWhite

Riesling

Zinfandel

CabernetSauvignon

Nebbiolo

Gewurztaminer

BorgonaBlanca

CabernetSauvignon

Nebbiolo

Gewurztaminer

WhiteBurgundy

Syrah

Syrah

Merlot

Merlot

Technology Topics

Example: Wine Ontologiesstart with…Edit Distance Mapping(and other syntactical comparisons)

example wine ontologies and then multiple graphical structure mappings

Vinos

Wine

VinosRojos

VinonBlancos

RedWine

WhiteWine

BurdeosRojo

Tempranillo

Chablis

CheninBlanc

RedBordeaux

Tempranillo

Chablis

CheninBlanc

MezclaDeCabernet

Dolcetto

SauvignonBlanc

PinotNior

CabernetMerlot

Dolcetto

SauvignonBlanc

PinotNior

BorgonaRoja

RojoItaliano

Semillon

Muscat

RedBurgundy

ItalianRed

Semillon

Muscat

Chianti

Berbera

WhiteBordeaux

Sake

Chianti

Berbera

WhiteBordeaux

Sake

PetiteSirah

Sangiovese

Chardonnay

PinotGris

PetiteSirah

Sangiovese

Chardonnay

PinotGris

PinotNior

BlancoItaliano

Riesling

Zinfandel

PinotNior

ItialianWhite

Riesling

Zinfandel

CabernetSauvignon

Nebbiolo

Gewurztaminer

BorgonaBlanca

CabernetSauvignon

Nebbiolo

Gewurztaminer

WhiteBurgundy

Syrah

Syrah

Merlot

Merlot

Technology Topics

Example: Wine Ontologiesand then….multiple graphical structure mappings
slide14

Vinos

Wine

VinosRojos

VinonBlancos

RedWine

WhiteWine

BurdeosRojo

Tempranillo

Chablis

CheninBlanc

RedBordeaux

Tempranillo

Chablis

CheninBlanc

MezclaDeCabernet

Dolcetto

SauvignonBlanc

PinotNior

CabernetMerlot

Dolcetto

SauvignonBlanc

PinotNior

BorgonaRoja

RojoItaliano

Semillon

Muscat

RedBurgundy

ItalianRed

Semillon

Muscat

Chianti

Berbera

WhiteBordeaux

Sake

Chianti

Berbera

WhiteBordeaux

Sake

PetiteSirah

Sangiovese

Chardonnay

PinotGris

PetiteSirah

Sangiovese

Chardonnay

PinotGris

PinotNior

BlancoItaliano

Riesling

Zinfandel

PinotNior

ItialianWhite

Riesling

Zinfandel

CabernetSauvignon

Nebbiolo

Gewurztaminer

BorgonaBlanca

CabernetSauvignon

Nebbiolo

Gewurztaminer

WhiteBurgundy

Syrah

Syrah

Merlot

Merlot

Technology Topics

Example: Wine Ontologiesand finish with multiple filters to resolve remaining discrepancies

ontrapro ongoing research activities
Ontrapro – ongoing research activities

We are integrating learning techniques with the alignment heuristics to create situationally optimal ontology aligners.

coordination overhead comparison

1

1

1

2

2

2

3

3

3

4

4

4

Coordination Overhead Comparison

Effort, considerations, and time to wait -- the number of interfaces developed by some party other than the service creator to learn before one may connect each service to each other, by agents, SOAP, etc.--are least for ONTRAPRO automatic alignment approach.

Protocol

XML

OWL

Manual

Specialized Benefits

Brittle-to-change

No domain notion

Manual

Tradeoff for Generic

Change-w/-Standard

Single domain scope

Automatic

Specialized Benefits

Change-at-Will

Multi-domain ready

(no prior)

Domain Standard

Ontrapro

O(n2)

O(n)

O(1)

slide17

Software Agents

Technology Topics

software agents emaa
Software Agents – EMAA
  • ATL has an agent architecture used on dozens of DARPA and DoD Service Lab contracts: The Extensible Mobile Agent Architecture (EMAA) is a suite of mature software modules.
  • XMDR Use Cases:
    • Support for Development of a “Universal” Grid
      • Unspecific (generic) domain
      • Resources discovered, navigated, composed together via some intermediating semantic metamodel.
    • Data Aggregation
      • Information retrieval by linking together resources into aggregate data reports.
      • Collating resources already registered within the XMDR registry.
  • ONTRAPRO+Agents approach compliments the XMDR’s intermediating semantic metamodel. Our approach uses Agents as work-flow mechanism.
software agents emaa cont
Software Agents – EMAA (cont.)
  • ATL’s EMAA agents:
    • Comprise of a series of net-centric application resources described in OWL-S as web services
    • Are composed of building blocks, “tasks” that interconnect multiple web-service resources
      • Composition has been done dynamically through metadata planning, MPAC (Meta Planning for Agent Composition).
    • EMAA agents usually extract information reports by aggregating outputs of some services and feeding these reports as inputs to others
    • EMAA agents can also “enact” upon data by executing a pre-built processing instruction
    • ATL builds “Semantic Web Agents”, described in terms found from a well-connected ontology
    • Additional agent research at ATL also available for use: agent learning, adaptivity, and collaboration
dynamic agent composition example charting ship route in a channel
Dynamic Agent Composition (example: charting Ship route in a channel)

Y-axis has available sensors as web-servicesX-axis has route-planners that use available sensor inputs

dynamic agent composition example charting ship route in a channel21
Dynamic Agent Composition(example: charting Ship route in a channel)

Meta Planning of Agent Composition enables dynamic route planning

slide22

Static and Dynamic Analysis

of Applications

Technology Topics

dynamic and static application analysis
Dynamic and Static Application Analysis
  • XMDR Use Case: Support a Data Grid
    • Ontrapro reduces the need for standards for interoperability
  • ATL has research interests in Dynamic and Static analysis applications to extract application’s inputs, outputs, and design intent
    • API & Static Analysis:
      • Examine software source-code documentation.
      • Design documents in UML
      • Whitepapers, conference papers, journal entries through NLP
    • Dynamic Code Analysis
      • At run-time analysis of object creation, method invocation
slide24

Service 2 Service

Search and Retrieval

Technology Topics

service 2 service matchmaking
Service 2 Service Matchmaking
  • XMDR Use Case: Discovery, Location and Retrieval
    • Retrieve part or all of a terminology/concept structure
    • Retrieval based on related items: “data element, property, concept, class, domain, context, classification scheme, ontology”
    • Retrieve identity of registrar responsible for it
  • Ontrapro creates a comparison of ontologies as a result of attempting to align them
    • This allows us to find similar services semantically
    • We have additional algorithms to match service descriptions
slide26

Explanation Generation

Technology Topics

explanation generation
Explanation Generation
  • XMDR Use Case: Help Support
    • “A client application pulls metadata from the MDR in order to provide online help for an application end user.”
    • Provided from the registered application directly
  • OWL-S  Natural Language
    • Descriptions of a semantic web service converted to a human-understandable paragraph on what it does
    • Description of process
      • What we intend to have happen
      • What actually happened
  • Automatic and dynamic:
  • extraction of web-service spec/intent;
  • comparison with actual result;
  • generation of explanation;
  • NLP output
service navigation and execution tools
Service Navigation and Execution Tools
  • XMDR Use Case: Navigation
    • Applications uses MDR to support navigation of registered data elements and concepts between data elements
  • Execution Tools
    • Users could navigate components found in an MDR registry
    • Users could directly execute components if desired, from within the COACH framework
    • Parameter study and optimization tools built in

COACH

cognitive algorithm composition handler coach concept
COgnitive Algorithm Composition Handler (COACH) concept
  • XMDR Use Case: Navigation
    • Applications use MDR to support navigation of registered data elements and concepts between data elements
  • ATL is developing the COACH framework concept
    • Users could navigate components found in an MDR registry
    • Users could directly execute components if desired, from within the COACH framework
    • Parameter study and optimization tools built in

COACH

information interpretation and integration conference i 3 con
Information Interpretation and Integration Conference (I3CON)
  • Experiment Participants

Jerome Pierson (INRIA)

John Li (Teknowledge)

Lewis Hart (AT&T)

Marc Ehrig (University of Karlsruhe)

Todd Hughes (LM ATL)

  • Guest Speakers

Ben Ashpole (LM ATL)

Bill Andersen (Ontology Works)

Mike Pool (Information Extraction and Transport)

Yun Peng (University of Maryland Baltimore County)

Mike Gruningner (University of Maryland)

information interpretation and integration conference i 3 con32
Information Interpretation and Integration Conference (I3CON)
  • Experiment Participants

Jerome Pierson (INRIA)

John Li (Teknowledge)

Lewis Hart (AT&T)

Marc Ehrig (University of Karlsruhe)

Todd Hughes (LM ATL)

  • Guest Speakers

Ben Ashpole (LM ATL)

Bill Andersen (Ontology Works)

Mike Pool (Information Extraction and Transport)

Yun Peng (University of Maryland Baltimore County)

Mike Gruningner (University of Maryland)

  • August 24-26, 2004 in Gaithersburg, MD
  • ATL organized
  • Published paper
  • Positive Review in AFRL/IF Directorate Monthly Web Newsletter
evaluation of ontology tools workshop
Evaluation of Ontology Tools Workshop
  • Participants:
  • Customers:
  • Dan Adams (NGA)
  • Sam Chance (NRL)
    • Kevin Keck (BNL)
  • U.S:
  • Mark Mayberry (Mitre)
  • Chris Priest (HP)
  • International:
  • Willa Wei (MDA)
  • Toru Ishida (KU)
  • Shigoeki Hirai (AIST)
  • Marc Ehrig (KU)
  • Jerome Euzenat (INRIA)
  • Alex Smirnov (RAS)
  • Marco Neuman (DIT)
  • Ian Horrocks (UM)
  • Jeremy Carrol (HP)

Contest results:

atl and xmdr goals
ATL and XMDR: Goals
  • ATL has prototypes and concepts that can help solve some of the key XMDR use cases (as shown in previous slides):
    • ONTRAPRO, COACH, EMAA
  • ATL can build Semantic Web Services out of each of these technologies and enable XMDR to use them as parts of its architecture and prototype.
    • Support initial generation of ontology translators between web-services and service model
    • Support initial generation of ontology content for the prototype
    • Support easy migration of web-services from one version of service model to the next revision.
  • ATL technology compatible to the XMDR framework:
    • Usage of OWL, OWL-S, RDF and RDFS
    • Usage of SWRL, UML, and RDQL.
    • Most of our software written in Java.
  • ATL is actively seeking solutions to the other XMDR use cases.
ontrapro accomplishments to date
Ontrapro accomplishments to Date
  • New, integrated alignment algorithms
    • Syntax aligners
    • Lexical aligners
    • Structural Aligners
    • Preprocessors
    • Filters
  • Enhanced display
  • Experimentation and evaluation of alignment performance
cognitive algorithm composition handler coach
Cognitive Algorithm Composition Handler (COACH)
  • COACH (Cognitive Algorithm Composition Helper)
    • Stable architecture
    • Composable experiment management
    • Enhanced GUI
    • Increased linkage to meta data
    • Serialization support
    • New runners and optimizers
    • Fine grained control over search spaces
    • Cluster extension
  • Enables massive parameter studies, optimizations, and simulation evaluations
  • Enables experience sharing between learners