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Ontology and Agent based Approach for Knowledge Management

Ontology and Agent based Approach for Knowledge Management. Defense of PhD Thesis Michal Laclav í k Supervisor: Ing. Ladislav Hluch ý PhD. Outline. Motivation State of the Art Objectives Methodology and Tools Agent Knowledge Model – Models, Methodology, Library Experience Management

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Ontology and Agent based Approach for Knowledge Management

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  1. Ontology and Agent based Approach for Knowledge Management Defense of PhD Thesis Michal Laclavík Supervisor: Ing. Ladislav Hluchý PhD.

  2. Outline • Motivation • State of the Art • Objectives • Methodology and Tools • Agent Knowledge Model – Models, Methodology, Library • Experience Management • Applications • Conclusion Bratislava, 12th January 2006

  3. Motivation and State of the Art • MAS ispowerful paradigm for distributed or heterogeneous systems • MAS need Knowledge Support and Semantics • MAS need Connection with Existing Commercial Standards • Agent Technology Roadmap: Current MAS Systems – lack of Internal Agent Knowledge Model, lack of interconnection with semantic web results (knowledge model representations) and commercial standards • Focus on Agents and Knowledge representation (Ontologies) • Knowledge Management and Experience Management as application domains Bratislava, 12th January 2006

  4. State of the Art - Agents • Agent Definition: An agent is a computer system capable of flexible autonomous action in a dynamic, unpredictable and open environment. (LUCK 2003) • MAS Standards: FIPA, MASIF • Related to agent communication, agent platforms • No standards for internal agent knowledge model with available implementations Bratislava, 12th January 2006

  5. State of the Art - Agents • Architectures: • Reactive Architecture • No specification of knowledge model, behavior of agent is based on implemented responses to environment states • Belief Desire Intention Architecture – BDI • Belief – represents knowledge model, available some implementations based on logic programming, not used in FIPA compliant MAS • Behavioral Architecture • FIPA compliant MAS are based on such architecture • No specification of Internal Agent Knowledge model – depend on agent designer and developer • JADE Agent System • Support for ontologies based on FIPA-SL (Similar to First Order Logic) • No Query engine • No Storage • No Inference Bratislava, 12th January 2006

  6. Ontologies Knowledge Representation OWL-DL compatible with Description Logic Query and Storage Engines available RDF, OWL, RDQL based Application domain Knowledge Management (KM) is the process through which organizations generate value from their intellectual and knowledge-based assets (Source: CIO Magazine) Experience Management is special kind of KM – based on “lessons learned” Reasoning Actions Pragmatics Knowledge Semantics Information Data Syntax Characters (Bergman, 2002, Experience Management) State of the Art – Ontologies, Knowledge Bratislava, 12th January 2006

  7. User requests Displaying results Agent 1 XML, XML-RPC, SOAP ACL KM Agent 3 FIPA ACL, KIF, FIPA-SL, FIPA-RDF IIOP, HTTP, SMTP Agent 2 KM FIPA ACL, RDF/OWL, RDQL Knowledge Storage Querying Problem Specification Graphical User Interface Multi Agent System External System Knowledge Model Knowledge Base Directory Facilitator Bratislava, 12th January 2006

  8. State of The Art Conclusion • Focus on software, intelligent and FIPA compliant agents • Providing better semantic infrastructure (ontologies, knowledge models) • Apply basic principles of software and knowledge engineering • Make stronger connection between MAS and existing commercial technologies Bratislava, 12th January 2006

  9. Thesis Objectives • Design of Agent Architecture using Ontology based Knowledge Model • Design of Software Development Methodology for creation of Agents with Ontology based Knowledge Model • Design of Generic Ontology Model for Experience Management with extension to different application domains. • Design & Development of Software Library for building Intelligent Agents with Ontology Knowledge Model with possibility to plug agents to existing commercial technologies • Design and Development of user friendly Knowledge Presentation. • Evaluation of Results on real pilot operation. Bratislava, 12th January 2006

  10. Used Methods and Methodologies • Knowledge management, system design • Unified Modeling Language – UML • CommonKADS, MAScommonKADS • Protégé as Tool for CommonKADS • Formal methods for describing ontology based models • Description Logic • Graph Ontology representation Bratislava, 12th January 2006

  11. Used Tools and Software • Protégé Ontology Editor • Support for OWL ontology format • Can be used as modeling tool • JADE (Java Agent DEvelopment Framework) • Most developing MAS framework • Compliant with FIPA standards • Jena – Semantic Web Framework for Java • Support for OWL – best available OWL API • Support for RDQL model querying Bratislava, 12th January 2006

  12. Agent Knowledge Model Objective: Design of Agent Architecture using Ontology based Knowledge Model

  13. Based on Events, Resources, Actions, Actors, Context Formally Described using Sets, Description Logic (compatible with OWL-DL), Graph Representation Actor Context updating function/algorithm (Actor Environment State) CAnew = fC(ea,CAold) Resources updating function/algorithm (result of fulfilled actor goals) RAnew = fR(CAnew,RAold) Agent Knowledge Model Bratislava, 12th January 2006

  14. Software Development Methodology Objective: Design of Software Development Methodology for creation of Agents with Ontology based Knowledge Model

  15. Development Methodology (Knowledge Model) • Extending Model with Protégé Editor following CommonKADS models • Organizational or Environment Model • Task Model • Agent or Actor Model • Includes implementation of algorithms for context and resource updating • Results • Ontology developed in Protégé which can be exported in OWL format. • Concrete Algorithms for each actor (often algorithms are similar or same) which updates actors' context CAnew and resources RAnew. Bratislava, 12th January 2006

  16. Development Methodology (System Design) • UML Diagrams for concrete Application Domain • Use Case Diagram • for each agent • agent is taken as system boundaries • Sequence Diagram • Communication among agents • Class Diagram • Behaviors are described as methods Bratislava, 12th January 2006

  17. Agent Software Library Objectives: Design of Agent Architecture using Ontology based Knowledge Model Design & Development of Software Library for building Intelligent Agents with Ontology Knowledge Model with possibility to plug agents to existing commercial technologies

  18. Support for OWL based Agent Knowledge Model Support for XML-RPC connection to receive event and send plain XML Support for agent communication using FIPA ACL with OWL and RDQL as content languages Support for Presentation of Ontological Knowledge (RDF/OWL => plain XML + XSL => HTML) JADE and Jena Integration Available on JADE official website to MAS community Agent Software Library Bratislava, 12th January 2006

  19. Agent Library Example Bratislava, 12th January 2006

  20. Support for Knowledge and Experience Management Objective: Design of Generic Ontology Model for Experience Management with extension to different application domains.

  21. Extended Agent Memory Model Workflow Related WfInstance, WfActivity ActiveHint Sub class of resource Representation of Experience Employee Extension of Model for Experience Management Bratislava, 12th January 2006

  22. Actor (Employee) Context updating algorithm CAnew = fC(ea,CAold) Resources (Active Hint) updating algorithm RAnew = fR(CAnew,RAold) Algorithms for EM Extension Bratislava, 12th January 2006

  23. All depends also on Active Hints Templates count – this does not grow too fast. 1st Case: Constant – final count of context elements (1-6) 2nd Case: O(n) – based on resource/event count in Memory 3rd Case: O(n2) – based on 2 loops: events/resources, similar resources experimental solution because algorithm used other software e.g. Jena with RDQL – it was hard to prove complexity different way. Complexity of algorithms Bratislava, 12th January 2006

  24. Similarity of Ontology Individuals Weighted matching of properties Similar to CBR algorithm Weighted Euclidian Distance Resource Similarity (3rd Case) • sim({res1,res2}) = fsim("{propi} º propertyi.Resource({res1}) Ç "{propj} º propertyj.Resource({res2}) Ç {propi} º{propj} Ç {propi} Î DomainClass Ç DomainClass Í Domain Ç ${simWeight} Î SimilarityWeight Ç domainClass.SimilarityWeight( DomainClass) Î {simWeight} Ç {weight} º weight .SimilarityWeight( DomainClass) Î {simWeight};Sij{weight}/n) Bratislava, 12th January 2006

  25. Presentation of Ontology based Knowledge Objective: Design and Development of user friendly Knowledge Presentation.

  26. Ontology Tree Browse window Graph XSL Transformation RDF/OWL => Plain XML + XSL => HTML Infrastructure to receive plain XML using XML-RPC Presentation of Ontology based Knowledge Bratislava, 12th January 2006

  27. Applications Objective: Evaluation of Results on real pilot operation.

  28. Title: Platform for Organizationally Mobile Public Employees Duration: Sep 2002- Dec 2004 Knowledge Management to support employees Workflow based Administration Processes To support Employee Mobility in organization Agent Architecture based on autonomous co-operating agents Interaction Layer Process Layer Pellucid 5FP IST Project Pellucid Architecture Pellucid Agents Bratislava, 12th January 2006

  29. CDG, Genoa, ItalyTraffic Light Management MMBG, Sanlucar, SpainProject Management SADESI, Seville, SpainTelephone Incidence Resolution Pellucid Applications Bratislava, 12th January 2006

  30. Work on new EMBET architecture Current state: User Assistant Agent in K-Wf Grid uses model presented in thesis. Algorithms presented in chapter 5 were reused with same improvements and modifications. Architecture is not Agent based but users of system are modeled as actors. Knowledge Model, its implementation and modified algorithms presented in thesis are used Title: Knowledge-based Workflow System for Grid Applications Objectives: To support workflow construction and execution with Knowledge Duration: Sep 2004 - Feb 2007 K-Wf Grid 6FP IST Project Bratislava, 12th January 2006

  31. Conclusion and Future Work

  32. Conclusion (1) • The most significant scientific achievements • Agent knowledge model • Applicable in any discrete environment where actors need to be modeled • Can be expressed by ontology, sets or description logic • Such model was found useful for: • Simple goal oriented agents • Knowledge Management Solution based on Agents (Pellucid) • Experience Management Solution non agent based (EMBET System) • Development Methodology • Speed up Knowledge based Agent development for concrete application domains Bratislava, 12th January 2006

  33. Conclusion (2) • The most significant development achievements • Agent Library • Support for OWL based Agent Knowledge Model • Support for XML-RPC connection to receive event and send plain XML • Support for Presentation of Ontological Knowledge (RDF/OWL => plain XML + XSL => HTML) • Support for agent communication using FIPA ACL with OWL and RDQL as content languages • JADE and Jena Integration • Available on JADE official website to MAS community (August - December 2005 – 314 downloads) Bratislava, 12th January 2006

  34. Conclusion (3) • Extension of Work for Experience Management • Model • Algorithms • Projects • Motivation for solving problems in real Application • Evaluation of Thesis results Bratislava, 12th January 2006

  35. Future work • RAPORT APVT project (01/2005-12/2007): Research and development of a knowledge based system to support workflow management in organizations with administrative processes • model and algorithms will be reused and extended • K-Wf Grid EU 6FP RTD IST project (2004-2007) • evaluation on more applications, improvement of context detection • NAZOU SPVV Project (09/2004-11/2007): Tools for acquisition, organization and maintenance of knowledge in an environment of heterogeneous information resources • OnTeA semantic annotation – not directly related but can be used for context detection Bratislava, 12th January 2006

  36. Thank you ! Thank You for you attention Many Thanks to my supervisor Many Thanks to my colleagues Many Thanks to the Reviewers for their helpful and constructive comments and for reading my thesis

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