1 / 14

Use of Ontology in Virtual Organizations for Environmental Risk Management II SAS, Bratislava

Use of Ontology in Virtual Organizations for Environmental Risk Management II SAS, Bratislava Ladislav Hluchý Zolt á n Balogh Michal Laclavík Smolenice, October 20-23, 2003. Outline. V irtual Organization Identif y needs for VO in ERM CommonKADS Brief Introduction

gilles
Télécharger la présentation

Use of Ontology in Virtual Organizations for Environmental Risk Management II SAS, Bratislava

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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 Smolenice, October 20-23 2003

  3. 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. Smolenice, October 20-23 2003

  4. 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 Smolenice, October 20-23 2003

  5. 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. Smolenice, October 20-23 2003

  6. CommonKADS: Models • Organizational Model, by which we describe the major features of VO, it’s 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. Smolenice, October 20-23 2003

  7. CommonKADS Smolenice, October 20-23 2003

  8. Ontology Ontology defines meaning of terms and their relations present state HTML => XML => RDF => Ontology (DAML+OIL) => OWL • CommonKADS models can be expressed by ontology (especially Knowledge Model) • Human knowledge can be partially described by ontology => computer understandable way Smolenice, October 20-23 2003

  9. Ontology: Representation • UML Class Diagram • Any Object Oriented Language • RDF Based • DAML+OIL • OWL <rdf:RDF xmlns:XMLSchema ="http://www.w3.org/2000/10/XMLSchema#" xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:daml_oil ="http://www.daml.org/2001/03/daml+oil#" xmlns:ontology ="http://pellucid.ui.sav.sk/ontology#" xmlns:rdfs ="http://www.w3.org/2000/01/rdf-schema#" xmlns ="http://pellucid.ui.sav.sk/ontology#" > <daml_oil:Class rdf:ID="Organization"> </daml_oil:Class> <daml_oil:ObjectProperty rdf:ID="employees"> <daml_oil:range rdf:resource="#Employee"/> <daml_oil:domain rdf:resource="#Organization"/> <daml_oil:domain rdf:resource="#Role"/> </daml_oil:ObjectProperty> <daml_oil:ObjectProperty rdf:ID="goal"> <daml_oil:range rdf:resource="#Goal"/> <daml_oil:domain rdf:resource="#Organization"/> <daml_oil:domain rdf:resource="#Role"/> </daml_oil:ObjectProperty> <daml_oil:Class rdf:ID="Goal"> </daml_oil:Class> <daml_oil:Class rdf:ID="Employee"> <rdfs:subClassOf rdf:resource="#Person"/> </daml_oil:Class> </rdf:RDF> Smolenice, October 20-23 2003

  10. Ontology: Tools • Modeling • Protégé • OilEd • Implementation • Java • HP Jena Library (DAML+OIL, RDQL, RDF) • JADE (Software Agent Platform) Smolenice, October 20-23 2003

  11. Protégé: Part of the Basic Model of VO Smolenice, October 20-23 2003

  12. 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, …) Knowledge Modeling for VO in ERM App-specific Customization of GMVO for ERM GMVO for ERM VO General Model Smolenice, October 20-23 2003

  13. Data sources meteorological radars • External sources of information • Global and regional centers GTS • EUMETSAT and NOAA • Hydrological services of other countries surface automatic meteorological and hydrological stations systems for acquisition and processing of satellite information Storage systems databases High performance computers Grid infrastructure meteorological models hydrological models hydraulic models Users Flood crisis teams • river authorities • energy • insurance companies • navigation • meteorologists • hydrologists • hydraulic engineers • media • public VO for Flood Forecasting Smolenice, October 20-23 2003

  14. We have identified why we needknowledge 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 Conclusion Smolenice, October 20-23 2003

More Related