SAKE: A Methodology for semantic-enabled Agile Knowledge-based E-Government

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SAKE A Methodology for semantic-enabled Agile
Knowledge-based E-Government

• Spyridon Ntioudis, Dimitris Apostolou, Gregoris
  Mentzas

2
Outline

• Motivation and State-of-the-Art
• The SAKE KM methodology
• Objectives
• Overview
• Current Status and Future Work

3
Motivation

• Knowledge has been and is still governments most
  important resource.
• Knowledge is regularly localised or even personal
  and difficult to share
• not necessarily available anywhere, anytime for
  anybody
• a lot of wheel reinventing in public
  administration
• the processing of unpredictable requests and
  exceptions arises unanticipated "knowledge needs
• Imperative need for a KM methodology for the
  public sector that will assist in better
  management of knowledge in order to retain the
  high quality and homogeneity of the public
  administration decision making processes

4
Landcape of KM methodologies in the Public Sector

• Know-Net Method Mentzas et al, 2002
• Greek Ministry of Finance
• CommonKADS methodology Schreiber et al.,1999
• DECOR methodology Abecker, 2003
• Greek Social Security Institute (IKA)
• Knowledge Maps Eppler, 2003
• Australian Government entry point
• Knowledge Management Toolkit Tiwana, 2000
• United Nations Knowledge Management Methodology
  Eppler, 2003
• Community of Practice Practitioners Guide
  NAVSEA, 2001
• NAVSEA department of the United States of America
  Navy

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Analysis of KM methodologies in the Public Sector

• None addresses efficiently all issues pertinent
  to carrying out KM initiatives in the Public
  Sector
• Know-Net, CommonKADS, DECOR methods and the KMT
• ideal when starting from the level of a single
  business process, since their execution is
  business process oriented
• CoPPG
• the only one addressing systematically the
  crucial issue of collaboration among the large
  number of stakeholders involved in the
  development of KM initiatives in public
  administrations

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Outline

• Motivation and State-of-the-Art
• The SAKE KM methodology
• Objectives
• Overview
• Application and Future Work

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Main objectives of the SAKE KM methodology

• To support the
• design
• development
• deployment
• evaluation
• of a semantic-enriched knowledge infrastructure
  in the public sector

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Outline

• Motivation and State-of-the-Art
• The SAKE KM methodology
• Objectives
• Overview
• Application and Future Work

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Overview of the SAKE Methodology
Step 1
Step 2
Step 4
Step 6
Step 3
Step 5
Detailed Knowledge sources analysis
Deployment of the SAKE solution
Evaluation of the SAKE solution
Knowledge As-Is Analysis
Description of selected focus area
Development of a trial plan

• Knowledge audit
• Selection of most promising focus area(s) and
  target solution
• Processes
• People
• Knowledge
• KM business case

• Detailed time and resource plan
• Assessment criteria
• Identify roles and actions for each stakeholder

• Qualitative and quantitative
• Evaluation framework for decision making quality
  assessment in public administrations
• Decision making quality ontology instantiation

• Detailed knowledge sources analysis

• Task analysis (process breakdown)
• Community people analysis
• Knowledge assets analysis

• Process and profile ontology
• PA ontology
• Information domain ontology

• Enhancement of bp models
• CoP building
• Content annotation

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Facts wrt the development of the SAKE methodology

• No reinventing the wheel
• based upon two existing methodologies
• Know-Net and
• CommonKADS methods,
• with extensions and modifications according to
  the e-government specificities that we wanted to
  cater

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Overview of the SAKE Methodology
Step 1
Step 2
Step 4
Step 6
Step 3
Step 5
Detailed Knowledge sources analysis
Deployment of the SAKE solution
Evaluation of the SAKE solution
Knowledge As-Is Analysis
Description of selected focus area
Development of a trial plan

• Knowledge audit
• Selection of most promising focus area(s) and
  target solution
• Processes
• People
• Knowledge
• KM business case

• Detailed time and resource plan
• Assessment criteria
• Identify roles and actions for each stakeholder

• Qualitative and quantitative
• Evaluation framework for decision making quality
  assessment in public administrations
• Decision making quality ontology instantiation

• Detailed knowledge sources analysis

• Task analysis (process breakdown)
• Community people analysis
• Knowledge assets analysis

• Process and profile ontology
• PA ontology
• Information domain ontology

• Enhancement of bp models
• CoP building
• Content annotation

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Steps 1 2 _at_ a glance

• CommonKADS is the leading methodology that
  supports structured knowledge engineering

CommonKADs model set
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Overview of the SAKE Methodology
Step 1
Step 2
Step 4
Step 6
Step 3
Step 5
Detailed Knowledge sources analysis
Deployment of the SAKE solution
Evaluation of the SAKE solution
Knowledge As-Is Analysis
Description of selected focus area
Development of a trial plan

• Knowledge audit
• Selection of most promising focus area(s) and
  target solution
• Processes
• People
• Knowledge
• KM business case

• Detailed time and resource plan
• Assessment criteria
• Identify roles and actions for each stakeholder

• Qualitative and quantitative
• Evaluation framework for decision making quality
  assessment in public administrations
• Decision making quality ontology instantiation

• Detailed knowledge sources analysis

• Task analysis (process breakdown)
• Community people analysis
• Knowledge assets analysis

• Process and profile ontology
• PA ontology
• Information domain ontology

• Enhancement of bp models
• CoP building
• Content annotation

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Step 3 implementation method
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Step 3 implementation method
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Step 3.1 Detailed Specification of Identified KR
(1/2)

• Key terms
• Frequently appeared terms and/or rather abstract
  concepts that can easily be identified from the
  Knowledge Assets

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Step 3.1 Detailed Specification of Identified KR
(2/2)

• Competency questions
• Queries that the SAKE system should be able to
  answer

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Step 3 implementation method
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Step 3.2. Pilot-specific extensions of the SAKE
Ontologies (1/2)

• Ontology development guidelines based on
• Noy McGuinness , Ontology Development 101 A
  Guide to Creating Your First Ontology, Stanford
  KSL Technical Report KSL-01-05, 2001.
• Staab et al., Knowledge Processes Ontologies,
  IEEE Intelligent Systems, Vol.16, Issue 1,
  pp.26-34, 2001
• Ontology representation language
• OWL-DL
• Ontology Editor
• Any ontology editor that supports OWL-DL
• Protégé (http//protege.stanford.edu/) was
  adopted by the three pilots
• Open source software
• Supported by a large community of users and
  developers
• User friendly

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Step 3.2. Pilot-specific extensions of the SAKE
Ontologies (2/2)

• Outline of Ontology development guidelines
• Step I Determine the domain and scope of the
  ontology
• Step II Consider reusing existing ontologies
• Step III Enumerate important terms in the
  ontology
• Step IV Define the concepts and the concept
  hierarchy
• Step V Define the properties of concepts
• Step VI Create instances

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Step 3 implementation method
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Step 3.3. Business Process Semantic Analysis
Modelling (1/2)

• The outcome of this task is the SAKE Process
  Ontology for each pilot which provides
• a detailed description of the modeled process
  flow
• the dependencies between activities
• the information resources that are used as
  inputs/outputs of activities
• the roles responsible for performing activities
• the potential communication interactions in
  activities between the responsible role for
  carrying out the activity and a third party
• Implementation Tool
• OntoGov Service Modeller tool (http//sourceforge.
  net/projects/ontogov/)

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Step 3.3. Business Process Semantic Analysis
Modelling (2/2)

• Why OntoGov service Modeller?
• Direct storage of the Process model in OWL ?
  compliance with the rest of the SAKE ontologies
  developed in OWL
• Open source software
• Easy, intuitive graphical interface

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Outline

• Motivation and State-of-The-Art
• The SAKE KM methodology
• Objectives
• Overview
• Application and Future Work

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SAKE (Semantic-enabled, Agile, Knowledge-based
e-Government) project
Research and Technology Partners
Pilot Users
http//www.sake-project.org/
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Application of the SAKE methodology Outcomes in
LATA (1/2)

• LATA
• Ontology for the domain of General Binding
  Regulations on the usage of the City ward signs
• 44 concepts and 9 properties
• Ontology for the experts skills that are
  pertinent for participating in the group of
  experts for creating General Binding Regulations
• 159 concepts and 4 properties
• Population of global ontologies
• PA ontology 42 individuals
• DM Quality ontology 2 individuals
• Process model for the selected process of LATA

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Application of the SAKE methodology Outcomes in
LATA (2/2)

• Process model of LATAs process

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Application of the SAKE methodology Outcomes in
MEC (1/2)

• MEC
• Ontology for the domain of Higher Education
  Portfolio Alignment with World of Labour Needs
• 34 concepts and 10 properties
• Population of global ontologies
• PA ontology 26 individuals
• DM Quality ontology 2 individuals
• Process model for the selected process of MEC

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Application of the SAKE methodology Outcomes in
MEC (2/2)

• Process model of MECs process

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Application of the SAKE methodology Outcomes in
UMC (1/2)

• UMC
• Ontology for the domain of Management of
  education institutions material resources
• 166 concepts and 23 properties
• Population of global ontologies
• PA ontology 76 individuals
• DM Quality ontology 2 individuals
• Process model for the selected process of UMC

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Application of the SAKE methodology Outcomes in
UMC (2/2)

• Process model of UMCs process

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Overview of the SAKE methodology
Step 1
Step 2
Step 4
Step 6
Step 3
Step 5
Detailed Knowledge sources analysis
Deployment of the SAKE solution
Evaluation of the SAKE solution
Knowledge As-Is Analysis
Description of selected focus area
Development of a trial plan

• Knowledge audit
• Selection of most promising focus area(s) and
  target solution
• Processes
• People
• Knowledge
• KM business case

• Detailed time and resource plan
• Assessment criteria
• Identify roles and actions for each stakeholder

• Qualitative and quantitative
• Evaluation framework for decision making quality
  assessment in public administrations
• Decision making quality ontology instantiation

• Detailed knowledge sources analysis

• Task analysis (process breakdown)
• Community people analysis
• Knowledge assets analysis

• Process and profile ontology
• PA ontology
• Information domain ontology

• Enhancement of bp models
• CoP building
• Content annotation

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Current status - Future Work

• The participating public administrations have
  received the 1st SAKE prototype providing only
  basic functionality, i.e. no semantic-enabled
  functionality.
• Feedback from using the 1st prototype will be
  provided to the research and development partners
  within the second project year and before the
  finalisation of the 2nd SAKE prototype
• Finalisation of the evaluation framework
• Decision Making Quality Ontology specific metrics
  for each participating public administration

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The Decision Making Quality ontology
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Thank you for your attention