1. Semantic Web and Valued Information at the Right Time (VIRT) Curtis Blais Research Associate MOVES Institute Naval Postgraduate School clblais@nps.edu

2. Rich Semantic Track Semantic Interoperability Joint C3 Information Exchange Data Model Tactical Assessment Markup Language USW-XML MIP Mobility COP C-BML Autonomous Vehicle Command Language Savage Modeling and Analysis Language AT/FP AVCL/AUVW FCS GIG Plans and Orders JBML VIRT Research Agenda C2 Ontology -Concepts -Relationships -Rules/Constraints CEC JTM CMA

3. Semantic Web “ An extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. ” – Berners-Lee, et. al., 2001 Transforming documents to information (data in context) Enabling automated reasoning Equally accessible to human and software agents

4. The Evolving Web Web of Knowledge HTML/HTTP Resource Description Framework Extensible Markup Language Self-Describing Documents Foundation of the Current Web Proof, Logic and Ontology Languages Shared terminology Machine-Machine Communication 1990 2000 2010 J. Hendler presentation, W3C, 2001 Explicit semantics through standard languages

6. Semantic Web Stack Source: I. Herman: “Introduction to the Semantic Web,” 12 November 2003. http://www.w3.org/2003/Talks/0624-BrusselsSW-IH/26.html

7. C2 Ontology Too large to be built all at once Would take too long Would be too hard to gain widespread acceptance Evolutionary development Start small (e.g., tracks) Show community how it is done (methodology) Demonstrate benefits (and limitations) Create mechanisms for extension Dissertation: Ontological Foundation for Obtaining Valued Information at the Right Time in Semantically Rich Network-Centric Architectures

8. Topics Applying Semantic Web Technologies to the Tactical Assessment Markup Language (TAML) – ENS Candace Childers CS Thesis, June 2006 Sponsor: DoN Chief Information Officer, USW-XML Working Group Rich Semantic Track and Valued Information at the Right Time – Rick Hayes-Roth, NPS Information Systems Sponsor: NAVSEA PEO IWS6(CEC)

9. Technology Goal Demonstrate application of existing and emerging Semantic Web standards to Network-Centric Architecture Explore benefits and limitations of Semantic Web technologies Examples: Conceptual querying across multiple representations Combining multiple source data and drawing inferences across the data

11. From Feasibility to Practice ENS Childers thesis demonstrated feasibility and breadth of application of Semantic Web standards Tool-based demonstration (Protégé, Twinkle) Single representation of contacts (TAML) Single domain (USW) Static ontology Unified Application (Blais dissertation) Formalized abstraction of Track concept for general software implementation Mediation of multiple track representations across multiple domains Operator and problem-driven evolution of the ontology

12. Rich Semantic Track Abstract model of battlespace perceptions Logical theory enabling reasoning over collected data Common semantics underlying multiple systems and across multiple domains Global Command and Control System, Maritime Domain Awareness, Cooperative Engagement Capability, TAML, Joint Track Management, etc.

13. Rich Semantic Track (RST) Rich Semantic Track - conceptual hub for interchange and automated reasoning CEC Track Messages Joint Track Management Data Model CMA Maritime Information Exchange Model Other Track Data Models

14. Rich Semantic Track Track Beliefs Identity and Characteristics Dynamic State at Time T History of States (past “track”) Predicted States (future “track”) Meta-Information (applicable to each element of belief) Evidence Inferences Error and Uncertainty Estimates Temporal Qualifications Spatial Qualifications *Hayes-Roth. Towards a rich semantic model of Track: Essential Foundation for Information Sharing. NPS Research Paper. Monterey, CA. February 25, 2005.

15. Technologies to provide Valued Information at the Right Time Screens and marshals the “data storm” Assists in the filtering of information to provide guidance based on situational priorities Automatically adapts to the environment by inferring valued information Especially via modeling spatiotemporal “tracks” Other class models useful, too Personalizes, using context, role, and state What is VIRT?

16. The Basic Ideas Synchronize groups by having them operate on semantically aligned and high-value information Determine what concepts operators’ missions depend on and make those standard Notice what beliefs underlie mission plans and Courses of Action (COAs) Automatically inform operators when changes in data affect their beliefs and planning rationales

17. COIs Overall Vision: Model-based Communication Networks, VIRT and Rich Semantic Track Past Global Information Grid Past Present Future Present Future Present Past Present Future Past Future COIs Shared World Models Common Track Semantics State-full Network COI = Condition of Interest

18. Overall Vision: Model-based Communication Networks, VIRT and Rich Semantic Track Past Present Future Present Future Present Past Present Future Past Future Shared World Models Common Track Semantics State-full Network Valued Bits Global Information Grid

19. Model-based Communication Network (MCN) Instead of Stateless Networking, State-full Maintain shared state among collaborators State = current values of models, e.g. The route plan, position, velocity of an aircraft The current and future position and behavior of a unit The hypothesized position, status and intention of a system A shared world model is the goal Collectively, what the collaborators believe Distributed, replicated for efficiency Autonomously updated, through dead-reckoning Like a distributed blackboard of hypotheses Re-conceptualize Common Operational Picture Obviate “communication” of non-news Emphasize “information,” especially valuable information

20. Semantic Modeling and Condition Monitoring Strong semantic representations of Track data improve automated search, information filtering and reasoning Improved computer interpretation and processing of data provides better information products and reduces human processing load Semantics of military orders implies critical conditions of interest – automated derivation of COIs from orders creates valued information flows

21. CEC/VIRT Project Investigated mapping of CEC track data to RST conceptual model Constructed various RST representations for software and web-based implementations Formal logic representation for automating mappings across track data models Semantic Web representations for web-based data interchange and machine reasoning Constructed various web-based expressions of COIs

22. CEC/VIRT Conditions of Interest Significant change in expected motion of air tracks based on decision thresholds for the magnitude of change in expected position and expected velocity Significant change in Track ID information (e.g., from FRIEND to HOSTILE) Significant change in Track IFF information (e.g., change in IFF mode responses) Start and end of engagements Assumption/Operational Decision: No need to send CEC network-specific Cooperating Unit, Time, and Status messages

23. Air Target Tracking Scenario Results CEP-to-Track User Messages* Number of MessagesNumber of Bits Without VIRT With VIRT** Without VIRT With VIRT** Valued Bit Ratio Track Data1,8521412,483,712189,5040.076 Track ID1869,2163,0720.333 Track IFF121042,59200.000 Cooperating Unit 5520176,64000.000 Engagement Status 0000N/A Status0000N/A Time0000N/A Totals2,5431472,712,160192,5760.071 * From RTTS XML message stream** Dead Reckoning thresholds of 100m and 10m/sec Valued bits made up 7% of the total bits transmitted – bit traffic can be reduced by 93% if only valued bits are sent!

24. CEC/VIRT Simulation Event Graph Embedding a semantically rich knowledge base into a simulation framework for testing/experimentation with VIRT/RST concepts in a dynamic context Common Implementation Pattern

25. Contacts Curtis Blais clblais@nps.edu MOVES Institute, Naval Postgraduate School Monterey California 93943-5000 USA 1.831.656.3215 voice 1.831.656.7599 fax