Ontology and Agent based Approach for Knowledge Management

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					Ontology and Agent based
Approach for Knowledge
Management

          Defense of PhD Thesis
          Michal Laclavík
          Supervisor: Ing. Ladislav Hluchý PhD.
Outline
   Motivation
   State of the Art
   Objectives
   Methodology and Tools
   Agent Knowledge Model – Models,
    Methodology, Library
   Experience Management
   Applications
   Conclusion

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Motivation and State of the Art
   MAS is powerful 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


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    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


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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
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    State of the Art – Ontologies, Knowledge

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




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           Problem Specification
                                        Graphical User Interface


        Multi Agent System                 User requests
                                       Displaying results
                                                                XML, XML-RPC, SOAP     External
ACL          KM
                                                                                        System
          Agent 1          FIPA ACL,
IIOP,                 KIF, FIPA-SL, FIPA-RDF                Knowledge
HTTP,                                             Agent 3     Model
SMTP
                            KM

                        Agent 2            FIPA ACL,
                                        RDF/OWL, RDQL             Knowledge Storage
        Directory                                                         Querying    Knowledge
        Facilitator                                                                      Base



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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

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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.




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    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

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    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

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Agent Knowledge Model

 Objective:
 Design of Agent Architecture using
 Ontology based Knowledge Model
        Agent Knowledge Model
   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)




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Software Development
Methodology

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


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     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



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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
    Agent Software Library
   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




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Agent Library Example




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Support for Knowledge and
Experience Management

 Objective:
 Design of Generic Ontology Model for
 Experience Management with extension to
 different application domains.
     Extension of Model for
     Experience Management
   Extended Agent Memory
    Model

   Workflow Related
        WfInstance, WfActivity
   ActiveHint
        Sub class of resource
        Representation of
         Experience
   Employee




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    Algorithms for
    EM Extension
   Actor (Employee)
    Context updating
    algorithm
       CAnew = fC(ea,CAold)
   Resources (Active Hint)
    updating algorithm
       RAnew = fR(CAnew,RAold)




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Complexity
of algorithms
   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.


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       Resource Similarity (3rd Case)
   Similarity of Ontology       sim({res1,res2}) = fsim(
                                        "{propi} propertyi.Resource({res1}) 
    Individuals                         "{propj} propertyj.Resource({res2}) 
                                        {propi}  {propj} {propi} DomainClass 
   Weighted matching of                DomainClass Domain 
                                        ${simWeight} SimilarityWeight 
    properties                          domainClass.SimilarityWeight( DomainClass) {simWeight} 
                                        {weight} weight .SimilarityWeight( DomainClass) {simWeight};
                                        Sij{weight}/n
   Similar to CBR              )

    algorithm Weighted
    Euclidian Distance




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Presentation of Ontology
based Knowledge

 Objective:
 Design and Development of user
 friendly Knowledge Presentation.
        Presentation of Ontology
        based Knowledge
   Ontology Tree
       Browse window
   Graph
   XSL Transformation
       RDF/OWL => Plain XML +
        XSL => HTML
       Infrastructure to receive
        plain XML using XML-RPC




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Applications

 Objective:
 Evaluation of Results on real
 pilot operation.
Pellucid 5FP IST Project
                                                         Pellucid Architecture
   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                                      Pellucid Agents
    organization
    Agent Architecture based on
                                                                         Interaction Layer

    autonomous co-operating agents                       Process Layer




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     Pellucid Applications

   CDG, Genoa, Italy
    Traffic Light Management
   MMBG, Sanlucar, Spain
    Project Management
   SADESI, Seville, Spain
    Telephone Incidence
    Resolution


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        K-Wf Grid 6FP IST Project
   Title: Knowledge-based Workflow System for Grid
    Applications
   Objectives: To support workflow construction and
    execution with Knowledge
   Duration: Sep 2004 - Feb 2007



                          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

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Conclusion and Future Work
        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
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        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)




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     Conclusion (3)
   Extension of Work for
    Experience Management
       Model
       Algorithms
   Projects
       Motivation for solving problems in real
        Application
       Evaluation of Thesis results

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    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




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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