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

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


1
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

5
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

6
Outline
  • Motivation and State-of-the-Art
  • The SAKE KM methodology
  • Objectives
  • Overview
  • Application and Future Work

7
Main objectives of the SAKE KM methodology
  • To support the
  • design
  • development
  • deployment
  • evaluation
  • of a semantic-enriched knowledge infrastructure
    in the public sector

8
Outline
  • Motivation and State-of-the-Art
  • The SAKE KM methodology
  • Objectives
  • Overview
  • Application and Future Work

9
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

10
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

11
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

12
Steps 1 2 _at_ a glance
  • CommonKADS is the leading methodology that
    supports structured knowledge engineering

CommonKADs model set
13
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

14
Step 3 implementation method
15
Step 3 implementation method
16
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

17
Step 3.1 Detailed Specification of Identified KR
(2/2)
  • Competency questions
  • Queries that the SAKE system should be able to
    answer

18
Step 3 implementation method
19
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

20
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

21
Step 3 implementation method
22
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/)

23
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

24
Outline
  • Motivation and State-of-The-Art
  • The SAKE KM methodology
  • Objectives
  • Overview
  • Application and Future Work

25
SAKE (Semantic-enabled, Agile, Knowledge-based
e-Government) project
Research and Technology Partners
Pilot Users
http//www.sake-project.org/
26
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

27
Application of the SAKE methodology Outcomes in
LATA (2/2)
  • Process model of LATAs process

28
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

29
Application of the SAKE methodology Outcomes in
MEC (2/2)
  • Process model of MECs process

30
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

31
Application of the SAKE methodology Outcomes in
UMC (2/2)
  • Process model of UMCs process

32
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

33
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

34
The Decision Making Quality ontology
35
Thank you for your attention
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