Use of Ontology in Virtual Organizations for Environmental Risk Management

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• Use of Ontology in Virtual Organizations for
  Environmental Risk Management
• II SAS, Bratislava
• Ladislav Hluchý
• Zoltán Balogh
• Michal Laclavík
• Smolenice, October 20-23, 2003

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Outline

• Virtual Organization
• Identify needs for VO in ERM
• CommonKADS
• Brief Introduction
• Description of Models in CommonKADS
• Ontology
• Knowledge Modeling by Ontology
• Ontology Representation
• Tools
• Knowledge Modeling for VO in ERM
• Possible use of Ontology and CommonKADS in VO
  Modeling for ERM

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Why we need knowledge in ERM?

• Knowledge is more than simple data or
  information.
• DATA INFORMATION KNOWLEDGE
• Knowledge differs from data or information in
  that new knowledge may be created from existing
  knowledge using logical inference. If information
  is data plus meaning then knowledge is
  information plus processing.
• Knowledge provides not only raw information about
  crises or risk situations but can also directly
  support actors in ERM VO to perform proper
  actions in the right time.
• Knowledge closer describes human understanding of
  the world.
• Knowledge enables learning and reasoning about
  data.

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Virtual Organization

• VO is any pattern of organization based around
  distributed physical, human and knowledge
  resources, and (most usually) tied together via
  information technology systems that enable such
  resources to perform valued-added activities.
• The specific relevance of knowledge for VO is
  laid in supporting decisions and in the
  improvement of the logistics of information.
• Information system needs to understand
• structure,
• application domain,
• actors (e.g. users) and
• all distributed entities and interfaces
• Need to have knowledge

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CommonKADS

• CommonKADS (KADS knowledge analysis and design
  support) is a methodology for knowledge
  management and engineering. The main principles
  are
• to construct different aspect models of human
  knowledge
• to concentrate on the conceptual structure of
  knowledge (and only afterwards on programming
  details)
• to recognize a stable internal structure of
  knowledge by distinguishing specific knowledge
  types and roles
• and finally to proceed in a knowledge project in
  a controlled spiral way by learning from
  experiences.

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CommonKADS Models

• Organizational Model, by which we describe the
  major features of VO, its goals, and also
  problem domains (in our case problem domains of
  ERM),
• Task Model, by which we describe the global task
  layout in VO,
• Agent Model, by which we describe agents (can be
  humans, information systems or any other entity)
  which are capable of performing tasks in VO,
• Knowledge and Communication Model, which are
  created from the 3 above mentioned models and
  describes knowledge and communication between
  agents in the VO,
• Design Model, which we create from all the above
  mentioned models and by which we describe the way
  the system should be implemented.

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CommonKADS
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Ontology
Ontology defines meaning of terms and their
relations present state HTML gt XML gt RDF
gt Ontology (DAMLOIL) gt OWL

• CommonKADS models can be expressed by ontology
  (especially Knowledge Model)
• Human knowledge can be partially described by
  ontology gt computer understandable way

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Ontology Representation

• UML Class Diagram
• Any Object Oriented Language
• RDF Based
• DAMLOIL
• OWL

ltrdfRDF xmlnsXMLSchema "http//www.w3.org/20
00/10/XMLSchema" xmlnsrdf "http//www.w3.org/
1999/02/22-rdf-syntax-ns" xmlnsdaml_oil
"http//www.daml.org/2001/03/damloil"
xmlnsontology "http//pellucid.ui.sav.sk/ontolog
y" xmlnsrdfs "http//www.w3.org/2000/01/rdf-s
chema" xmlns "http//pellucid.ui.sav.sk/ontolo
gy" gt ltdaml_oilClass rdfID"Organization"gt
lt/daml_oilClassgt ltdaml_oilObjectProperty
rdfID"employees"gt ltdaml_oilrange
rdfresource"Employee"/gt ltdaml_oildomain
rdfresource"Organization"/gt ltdaml_oildomain
rdfresource"Role"/gt lt/daml_oilObjectPropertygt
ltdaml_oilObjectProperty rdfID"goal"gt
ltdaml_oilrange rdfresource"Goal"/gt
ltdaml_oildomain rdfresource"Organization"/gt
ltdaml_oildomain rdfresource"Role"/gt lt/daml_oil
ObjectPropertygt ltdaml_oilClass rdfID"Goal"gt
lt/daml_oilClassgt ltdaml_oilClass
rdfID"Employee"gt ltrdfssubClassOf
rdfresource"Person"/gt lt/daml_oilClassgt lt/rdf
RDFgt
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Ontology Tools

• Modeling
• Protégé
• OilEd
• Implementation
• Java
• HP Jena Library (DAMLOIL, RDQL, RDF)
• JADE (Software Agent Platform)

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Protégé Part of the Basic Model of VO
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Knowledge Modeling for VO in ERM

• General Model of VO
• Concept Organization Entities, Actors, Goals,
  Activities, Procedures
• Extension of GMVO for ERM
• Concepts Risk Factors, Risk Management
  Procedures, Environment Entities
• Application-specific Customization of VO for ERM
• Customization for concrete application (flood,
  air-polution, )

App-specific Customization of GMVO for ERM
GMVO for ERM
VO General Model
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VO for Flood Forecasting
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Conclusion

• We have
• identified why we need knowledge in VO ERM
• proposed CommonKADS methodology for knowledge
  modeling
• represented knowledge by ontology a way to
  interchange knowledge between machines and humans
• shown how to proceed with knowledge development
  for ERM VO