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SBIR Topic A04-105: An Ontologically-Based Data Fusion Model

SBIR Topic A04-105: An Ontologically-Based Data Fusion Model. Chris Matheus & Mitch Kokar Versatile Information Systems, Inc. cmatheus, mkokar@vistology.com www.vistology.com. Outline. JDL Model Motivation, objectives, requirements Meta-modeling framework IFRM – top level

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SBIR Topic A04-105: An Ontologically-Based Data Fusion Model

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  1. SBIR Topic A04-105: An Ontologically-Based Data Fusion Model • Chris Matheus & Mitch Kokar • Versatile Information Systems, Inc. • cmatheus, mkokar@vistology.com • www.vistology.com

  2. Outline • JDL Model • Motivation, objectives, requirements • Meta-modeling framework • IFRM – top level • IFRM – first step refinements • Ontologies and fusion • How many ontologies can be used? • Commercialization – an idea • Tasks • Backup slides

  3. JDL Model SOURCE PRE - PROCESSING LEVEL ONE LEVEL TWO LEVEL THREE PROCESSING PROCESSING PROCESSING OBJECT SITUATION THREAT REFINEMENT REFINEMENT REFINEMENT HUMAN COMPUTER INTERACTION WA - SOT assessment for Multi - source/sensor capability: LEVEL FOUR PROCESSING PROCESS Mature / Available Lab Demos / Not Fielded Research Required REFINEMENT SOURCES DATA FUSION DOMAIN USERS NATIONAL DISTRIBUTED LOCAL INTEL EW SONAR RADAR . . . DATA DATA BASE MANAGEMENT SYSTEM BASES SUPPORT FUSION DATABASE DATABASE * From JDL/DFG Data Fusion Model

  4. Fusion: JDL Model

  5. Motivation • JDL has seen a lot of attention • But mainly Level 1 has been tested • Recent emphasis on higher-level fusion made JDL insufficient • Moreover, JDL has been misinterpreted as a data flow model • Still needs a process model • Connected by “bus” – too flexible • Layered – too restrictive

  6. Reference Model - Impacts • Improve development efficiency • Enable application portability and scalability • Ease application adaptability • Improve system interoperability (NCW!) • Improve user productivity and portability • Promote vendor independence • Improve security • Reduce life-cycle cost

  7. Phase I Objectives • Analyze reference models, identify useful features • Propose adding process model to JDL • Formalize proposed model • Propose model evaluation approach and tools • Select application for validation in Phase II • Disseminate findings (papers, presentations) • Prepare an RFP for the OMG

  8. Model Requirements • Be descriptive – serve the fusion community • Capture data, functions, processes • Represent fusion processes – allow comparison of systems before they are built • Capture metrics of performance • Ontologically based – formal, computer processable semantics • Formal specification of the model itself • Have a place for capturing human-in-the-loop and user models • Have associated software tools • Compatibility with current models (as much as possible, but not more)

  9. Meta-Modeling Framework (MMF) Meta Model Definition of modeling elements Model Classes of objects and relations among classes (Need modeling elements to define models) Objects Objects of interest in the domain (instances)

  10. MMF – Software Engineering Meta Model UML Class, Association, … Class Diagrams Model Employee:Class, employedBy:Association Java Objects new Employee(“John”) new Employer(“IBM”) Objects

  11. MMF – Semantic Web Meta Model OWL Class, Property, … <Class Employee /> <Property employedBy /> Ontology Model Annotation (markup) <Employee John /> <Employer IBM /> Objects

  12. MMF – Information Fusion Meta Model IFRM IFRM Terms (Class, Property, Track, Object, Situation) Model of Fusion System System model (Track1:Track, O1:Object, TankB:Tank) Model Fusion System (run time) track51:Track1, tankA1:TankB, Objects

  13. Three Views

  14. Refinement: Product This is just a first refinement step, not even complete …

  15. OODA

  16. OODA in UML

  17. Refinement: Function(just an example)

  18. Ontology (Webster) • ontology 1. the branch of metaphysics dealing with the nature of being, reality or ultimate substance (cf. phenomenology) 2. particular theory about being or reality • phenomenology 1. the philosophical study of phenomena, as distinguished from ontology 2. the branch of a science that classifies and describes its phenomena without any attempt at metaphysical explanation • metaphysics 1. the branch of philosophy that deals with first principles and seeks to explain the nature of being or reality (ontology); it is closely associated with the study of nature of knowledge (epistemology)

  19. Ontology (cont.) • An explicit specification of a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them (Genesereth & Nilsson, 1997) • Definitions associate the names of entities in the universe of discourse (e.g., classes, relations, functions, or other objects) with human-readable text describing what the names mean, and formal axioms that constrain the interpretation and well-formed use of these terms. A statement of a logical theory. (Gruber) • An agent commits to an ontology if its observable actions are consistent with the definitions in the ontology (knowledge level). • A common ontology defines the vocabulary with which queries and assertions are exchanged among agents.

  20. Ontologies and IFRM • IFRM is at the same level as UML and OWL (from the linguistic point of view) • IFRM is a domain-specific (information fusion) modeling language • Ontology defines a conceptualization, a vocabulary … • Thus IFRM is an ontology, provided it is specified explicitly in a formal language (formal axioms)

  21. Ontologies and Fusion System Meta Model IFRM IFRM = Ontology of Fusion Model of Fusion System Specific ontology Model Fusion System (run time) Annotations Objects

  22. Commercialization Idea • Achieve acceptance by the fusion community • Achieve acceptance by the Government • Request for Proposals (RFP) for standards - the OMG • Work with OMG towards standard (IFRM) • Promote the standard • Build supporting tools • Result: Interoperability of various fusion systems!

  23. Tasks

  24. Backup Slides

  25. Ontology Languages • Web Ontology Language (OWL) • Lite • DL (Description Logics) • Full • OWL+SWRL (Semantic Web Rule Language) • RuleML • First Order Predicate Calculus (FOPL) • Higher Order Logics (HOL)

  26. OWL & SWRL SWRL Sem. Web Rule Language Web Ontology Language OWL RDF Schema RDFS RDF Resource Description Framework XML Extensible Markup Language Note: The layering of OWL on top of RDFS is not strict.

  27. How many ontologies? • Net Centric Warfare (NCW) requires full interoperability and thus communication • An agent commits to an ontology if its observable actions are consistent with the definitions in the ontology (knowledge level). • Two agents must commit to the same ontology in order to communicate • What if there are N agents? • One common ontology? Seems impossible! • N2 ontologies? Unmanageable! • Ontology mapping? Work in progress. • Core ontology + extensions? Compromise. • Somewhat similar idea to NATO’s Generic Hub (GH5) and C2 Information Exchange Data Model (C2IEDM)

  28. C2IEDM Core

  29. CANDIDATE-TARGET-LIST RULE-OF-ENGAGEMENT REPORTING-DATA CAPABILITY ACTION CONTEXT OBJECT-TYPE LOCATION OBJECT-ITEM GH 5 Core

  30. GH5 Core • A neat idea • Common core, extendable • Relatively rigorous • Substantial amount of knowledge • XML Schema available • Not an ontology • Not formal (no formal semantics) • Thus cannot be processed by logical tools • Not quite compatible with MMF (mixes Object and Model levels) • But looks like a good start towards an ontology

  31. VIS SAW Core Ontology SBIR Phase II, AFRL/IF Rome, Mike Hinman, John Salerno

  32. Battlefield Ontology

  33. What is “extension”? • Add classes • E.g., Battalion is a Unit • Add properties (relations) • E.g., Platoon is part-of Company • Add constraints • E.g., Soldier can be part of only one Platoon • But the result must be an ontology that is consistent

  34. Consistency con•sis•ten•cy agreement with what has already been done or expressed; conformity with previous practice [Webster’s] In logic: from P and not(P) can derive anything Inconsistency is a dangerous thing for autonomous agents!

  35. Inconsistency: Example • Battlefield Ontology happens to be consistent • Suppose we have • Constraint: Unit must have at least 8 Soldiers • Suppose we then extend it by adding: • Class: Group (sub-class of Unit) • Constraint: Group has 3 Soldiers • Inconsistency! • Easy to generate inconsistencies while developing ontologies • ConsVISor to the rescue: http://www.vistology.com

  36. Ontology Mapping GUI AT1 T1 AO1 O1 ATt Tt AOo Oo AD1 ADd AK1 AKk AS1 ASs S1 Ss DB1 DBd KB1 KBk Agents: commit to ontologies; negotiate mapping; use templates

  37. Ontology Mapping (cont.) • Need to map: • Classes to Classes • Properties to Properties • Objects to Objects • Constraint mapping implicit • May result in inconsistency! • ConsVISor to the rescue: http://www.vistology.com

  38. Why Use Ontologies? • Represent theories of potential objects and relations as ontologies (OWL) • Represent collected data as annotations in terms of ontology (OWL) • Formulate any queries about situations in OQL (OWL Query Language) • Use a general purpose OWL reasoner to answer queries • Use the trace of the reasoner to give an explanation to user • Multiple ontologies may need to be combined into one (fusion) • Data Association and Fusion • association of objects with ontologies/annotations • relations among objects within ontologies/annotations • combining ontologies/annotations using colimit of category theory Flexibility: Can use the same reasoners on any ontologies and annotations!

  39. Use Case: Ontology-Based Fusion

  40. Use Case: Fusion System Development

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