Data Fusion based on ontology model for Common Operational Picture using OpenMap and Jena semantic f - PowerPoint PPT Presentation

1 / 25
About This Presentation

Data Fusion based on ontology model for Common Operational Picture using OpenMap and Jena semantic f


Data Fusion based on ontology model for Common Operational Picture using OpenMap and Jena semantic f – PowerPoint PPT presentation

Number of Views:143
Avg rating:3.0/5.0
Slides: 26
Provided by: mariuszch


Transcript and Presenter's Notes

Title: Data Fusion based on ontology model for Common Operational Picture using OpenMap and Jena semantic f

Data Fusion based on ontology model for Common
Operational Picture using OpenMap and Jena
semantic framework
  • Capt. Mariusz Chmielewski
  • Computer Science Department,
  • Cybernetics Faculty ,
  • Military University of Technology

Presentation plan
  • Definition of modeling domain
  • NEC, Situation Awareness, Information
    Superiority, Common Operational Picture
  • Ontologies semantic models
  • Knowledge representation
  • Formal model of ontology
  • Languages of semantic networks
  • Application of ontologies for COP
  • C4I systems migration strategies
  • Method for model transformation
  • Environment architecture
  • Toolkit presentation
  • Future Development

Network Enabled Capabilities - Summary
  • Network Enabled Capabilities - military doctrine
    or theory of war
  • Based on information advantage, made available by
    the information technology, into a competitive
    combat advantage through the efficient network
    mechanisms delivered for geographically dispersed
  • Main statements
  • Efficient networked mechanisms (distinctly
    improve information flow and information sharing
  • Information sharing enhances the quality of
    transferred information and in result situational
    awareness of the battlespace
  • Shared situational awareness enables
    collaboration between sensors, actors and command
    centers improving synchronization, and reduces
    the communication delays which in result speed
    up decision process and increase mission

COP, SAW in definitions
  • Common Operational Picture is a single
    identical display of relevant information shared
    by more than one command. A common operational
    picture facilitates collaborative planning and
    assists all echelons to achieve situational

Situation awareness (SAW) can be understand as a
perception of environmental elements within a
volume of time and space, the comprehension of
their meaning, and the projection of their status
in the near future. SAW bounds perception and
environment critical to decision-makers in
complex, dynamic processes, in this case -
military command and control.
COP services
  • COP can be defined as a service or a set of
    services providing
  • Collection of recognized pictures
  • Data fusion and correlation of recognized
  • filtering technology for commander as also if
    available interoperability link to source C4I
  • COP supports current shared operation picture
    containing elements from
  • RAP (Recognised Air Picture),
  • RMP (Recognised Maritime Picture),
  • RGP (Recognised Ground Picture),
  • RLP (Recognised Logistics Picture)
  • Intelligence systems
  • Decision support procedures

Semantic Models, Ontologies
  • Origin of technology - Next stage of the Internet
    evolution, Semantic Web.
  • Semantic description can improve the way
    information is presented.
  • Semantic data representation - based on graph
    model - RDF concept of triples representing
    resources and data describing them.
  • An ontology is used as a tool for describing and
    representing selected knowledge branch that is
    medicine, finances, battlefield etc.

Model of ontology

Model of semantic network

Languages of semantic networks
  • dedicated languages
  • RDF, RDF-S, SHOE, DAMLOIL, OWL (Ontology Web
  • share the same theoretical framework extended
    towards Description Logics to provide knowledge
    representation and reasoning mechanisms.
  • In terms of the expressivity, these languages can
    be arranged in an order RDF, RDF-S, SHOE,
    DAMLOIL and OWL.
  • OWL provides sublanguages
  • OWL Lite - expressiveness is limited (restricted
    primitives list), but in this case the efficiency
    of reasoning is preferable,
  • OWL DL (Description Logics) - is as expressive as
    possible on the premise of preserving
    completeness and decidability of reasoning
  • OWL Full - is required for modeling domains using
    full spectrum expressiveness with no
    computational guarantees of reasoning.

Modeling Battlespace Ontology
  • The core element of the designed tool is usage of
    an ontology and therefore a semantic model
    representing the battlespace (UBOM) Unified
    Battlespace Ontology Model.
  • Development based on ontology modeling
    metodologies, contains
  • Definition of researched domain and boundaries of
  • Existing ontology (domain models) overview and
    decision of application
  • Definitions of elementary abstractions within the
    domain creation of important concept list
  • Concept taxonomy modeling - class definition and
    their hierarchy
  • Class property modeling identifying properties
    (slots) for classes (definition domain and range
  • Property restriction definition object type or
    datatype specification
  • Identification of instances and their
    classification within the class taxonomy

Elements of UBOM
  • Ontology development for the COP enabled tool
    required detailed C4I system datasources
  • aspect of visualization mechanism
  • GIS data standard used by the system.
  • Unified Battlespace Ontology Model (UBOM) has
    been divided into two parts
  • MIP JC3 model based ontology describing wide
    range of military operations and the whole domain
    of military units and equipment
  • Decision support ontology containing concepts
    of decision, variant and the rest of decision
    process realized on the battlefield

Overcoming ontology differences
  • Two kinds of ontology mediation
  • ontology mapping
  • Correspondences are stored separately from the
  • Mappings are not part of the ontologies
  • Correspondences can be used for, querying
    heterogeneous knowledge bases using a common
    interface or transforming data between different
  • ontology merging
  • Produces a new ontology which is the union of the
    source ontologies
  • Merged ontology encapsulates all the knowledge
    from the original sources
  • Described process must ensure that all
    correspondences and differences between the
    ontologies are reflected in the merged ontology.

Overcoming ontology diferences
  • Stages of semantic mapping
  • Importing the content of the ontologies to chosen
    ontology language
  • Normalization of defined vocabularies through
    elimination of lexical and syntactical
  • Similarity evaluation of ontology entities using
    defined set of parameters
  • Ontology quantity analysis - predefined sets of
  • Establishing correspondences between similar
    entities (concept thesaurus), in the form of
    semantic bridges linking similar concepts
  • Utilizing developed mappings for instance
  • Revision of prepared mappings for improvements

Datasources providing combat scenario
Legacy Polish Armed Forces C4I systems Szafran,
Dunaj, Podbial, Leba Simuators Scenario Zlocien
Construction of battlespace picture
  • GIS Data Sources
  • Layers
  • Semantic model
  • Filters
  • Decision support procedure

Components of developed solution
  • Distributed GIS environment
  • Ontology processing mechanizms
  • Data migration mechanisms

Integration -gt OSGi framework based on Eclipse
Equinox implementation
Components of developed solution
  • OpenMap - open GIS framework providing core
    mechanisms for COP visualization and semantic
  • NASA WorldWind 3d visualization toolkit
    providing a Earth 3D model with a satellite high
    quality pictures cover and detailed Earth terrain
  • JENA Semantic Framework framework dedicated to
    process semantic models stored in RDF, RDFS, DAML
    and OWL, supporting SPARQL query language, SWRL
    rules language, inference mechanisms and graph
  • JESS Rules rule based inference engine which
    uses an enhanced version of the Rete algorithm to
    process rules. Tool provides also a extended
    scripting language for Java allowing to build
    applications using imperative language.
  • Shrimp Visualization Toolkit a innovative
    method of visualization large scale data extended
    towards ontology models and implemented as a
    Protégé plugin Jambalaya.

Distributed GIS Environment
Ontology processing
  • Protege JENA Semantic Framework
  • process semantic models stored in RDF, RDFS, DAML
    and OWL
  • Automated modeling
  • supporting SPARQL query language,
  • SWRL implementation - rule language for semantic
  • Inference mechanisms
  • Graph persistence
  • Graph visualisations

Visualisation Enviroments NASA WorldWind
Visualisation Environments OpenMap
Ontology Tool for COP
Integrated COP
Ontology Processing
Overview compont
GIS Toolbox, Coordinates calculator
GIS Layers Decision support procedures
(No Transcript)
Future development
  • extending the presentation layer with 3D GIS
    projection of terrain based on JOGL and Java 3D
  • remote reconnaissance tool allowing better
    understanding of battlespace and current combat
  • development directions are mobile solutions
    providing CTP or COP solution
  • SOA based environment and specialized software
    platform (PDA, Smartphone) provide decision
    support tools on lower level command

Thank You
  • Author
  • Capt. Mariusz Chmielewski
  • email
  • Computer Science Department,
  • Cybernetics Faculty ,
  • Military University of Technology

ACKNOWLEDGEMENTS This work was partially
supported by grant Research Project No
Write a Comment
User Comments (0)