ATL & XMDR Technologies Overview (Developed and Future Pursuits)

1. 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/

2. 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

3. Advanced Technology Laboratories Overview Jim Marsh, Director (856) 792-9820 jmarsh@atl.lmco.com

4. 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?

5. 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

6. 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

7. 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

8. Technology Topics ATL R&D Interests

9. Ontology Mapping Technology Topics

10. 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

11. 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)

12. 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)

13. 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

14. 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

15. Ontrapro – ongoing research activities We are integrating learning techniques with the alignment heuristics to create situationally optimal ontology aligners.

16. 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)

17. Software Agents Technology Topics

18. 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.

19. 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

20. 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

21. Dynamic Agent Composition(example: charting Ship route in a channel) Meta Planning of Agent Composition enables dynamic route planning

22. Static and Dynamic Analysis of Applications Technology Topics

23. 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

24. Service 2 Service Search and Retrieval Technology Topics

25. 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

26. Explanation Generation Technology Topics

27. 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

28. Service Navigation and Execution Tools Technology Topics

29. 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

30. 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

31. 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)

32. 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

33. I3CON: Experiment Results (1)

34. I3CON: Experiment Results (2)

35. I3CON: Experiment Results (3)

36. 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:

37. 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.

38. Soothing Images for Question Time

39. Backup Vgs

40. Ontrapro accomplishments to Date • New, integrated alignment algorithms • Syntax aligners • Lexical aligners • Structural Aligners • Preprocessors • Filters • Enhanced display • Experimentation and evaluation of alignment performance

41. 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

42. Ontrapro Technology Status