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Ontology Engineering




                     Tutorial


            Dr. Elena Simperl
        Dr. Christoph Tempich
                  24.09.2008
Ontology Engineering
Presenters




                    Dr. Elena Simperl                                Dr. Christoph Tempich

I am working as a senior researcher in the areas of   I am a management consultant in the CP
semantic systems and technologies at STI              Information Technology. at Detecon International.
Innsbruck, University of Innsbruck.
                                                      Sectors
Sectors
                                                         Telecommunication, Automotive.
   ICT.
                                                      Functions
Functions
                                                         Enterprise Information Management.




                                                                                                          ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
   R&D.
                                                         Technology markets and innovation.
   Education and training.
                                                      Experience
Experience
                                                         Consulting at Detecon International and
   Vice director STI Innsbruck, STI International        Bearingpoint (KPMG).
    service coordinator for education.
                                                         More than 10 years Semantic Web research.
   8 years of experience in Semantic Web research
                                                         Workshops and more than 40 publications.
    and development
                                                         Two Innovation Awards for Semantic Web
   Management of more than 10 national and EU
                                                          applications.
    projects

           Page 1
Ontology Engineering
Management Summary
You will learn how to convince your CEO to start an ontology engineering initiative and
how to implement it successfully in your company.

   The core objective of Information Management is to enable informed decision making.
   Ontologies play an increasing role in holistically organizing enterprise information.

        In the tutorial we will position Ontology Engineering in the broader context of
                                Enterprise Information Management.


We will introduce the five steps - setup, requirements analysis, glossary creation,
  modeling, test - of our methodology for developing ontologies in an enterprise context.




                                                                                                                 ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
       For each step we will present the roles of participating actors, the methods and software available to
        guide and even partially automatize particular tasks, and metrics which can be used to assess the
        quality of the intermediary outcomes.

   In addition we will discuss best practices and guidelines related to critical aspects of
    Ontology Engineering:
       Modeling specific types of knowledge.
       Resolving conflicts in collaborative ontology building processes through argumentation.
       The automatic generation and learning of ontologies from existing unstructured data sources.
       Ontology engineering economics.


          Page 2
Content




1.   Motivation

2.   Enterprise Information Management

3.   Ontology Engineering Methodologies

4.   Ontology Development




                                             ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
5.   Useful Management and Support Methods

6.   Conclusion




      Page 3
Content




1.   Motivation
     Vision
     Customer Challenges and Benefits
     Ontology Engineering Definition
     What Do You Expect from This Tutorial?
     Tutorial Overview




                                              ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
      Page 4
Motivation
Vision
Seamless data integration across different data sources on the Web is a great challenge.
It also promises huge business opportunities and cost savings.

                  Web-scale data integration                           Description

                                                               Semantic technologies refer to
                                                                techniques to help a computer
                                                                program automatically
                                                                process and use arbitrary data
                                                                (and services) in a meaningful
                                                                way.
                                                               After a decade of intensive




                                                                                                 ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                research semantic
                                                                technologies seem to be the
                                                                best candidate to offer the
                                                                underlying technology for
                                                                Web-scale data integration.
                                                               Ontologies are a core enabler
                                                                of this vision.
                                                               Relevant terms: Web 2.0,
                                                                Web 3.0, Semantic Web
                                                                Services, Semantic Web.


         Page 5
Motivation
Customer Challenges and Benefits
Enterprise Information Management aims to handle the rapidly growing amounts of
information relevant to an enterprise business.

                     Challenges                                                  Benefits

   The amount of available and potentially relevant         A flexible information infrastructure which allows
    information is growing exponentially.                     to manage the growing amount of information.
   The time for decision making is decreasing               Flexible integration with business processes to
    continuously.                                             account for changing business requirements.
   Decision making is based on multiple highly              Up-to-date information for accurate decision
    heterogeneous, distributed and rapidly changing           making.
    information sources.




                                                                                                                   ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                             Fulfillment of regulatory requirements.
   The explosion of information is facilitated by
                                                             Timely and informed interaction with customers
    technical developments such as RFID , email,
                                                              responding to their needs.
    Web, Internet.
                                                             Identification of information leakage, customer
   Enterprises need an intelligent information
                                                              demands and cost drivers.
    infrastructure which delivers the right information
    at the right place in the right time.
   Data Governance is required for clear
    responsibilities.



         Page 6
Motivation
Ontology Engineering Definition
“the set of activities that concern the ontology development process, the ontology
life cycle, and the methodologies, tools and languages for building ontologies”.*

                                                               Methodologies:
                                                                   Distributed
                                                                   Centralized                  Ontology
                               Languages                                                         Development
                                   OWL                                                          Process

                                   RDF(S)                                                           Requirements

                                   SPARQL                                                           Evaluation




                                                                                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                                                     Documentation
                                                          Ontology Engineering

                               Ontology Life                                                 Application
                               Cycle:                                                        Scenarios
                                   Development                                                     Search
                                                                       Tools
                                   Maintenance                                                     Integration
                                                          Ontology development
                                                          Storage, reasoning,
                                                           alignment, Web interaction,
                                                           interfaces
*Source: Gómez-Pérez, A. et. al.: Ontological Engineering. Advanced Information and Knowledge Processing. Springer, 2003.
            Page 7
Motivation
What Do You Expect from This Tutorial?
Please tell us ...




                                         Examples

                 Content                               Personal Situation




                                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                         Presenters


                 Format                                   Objectives



                                         Interaction




        Page 8
Motivation
Tutorial Overview
In this tutorial we focus on the ontology development process and introduce
methodologies, applications scenarios and tools.

                                              Methodologies:
                                                 Distributed
                                                 Centralized       Ontology
                    Languages                                       Development
                       OWL                                         Process

                       RDF(S)                                          Requirements

                       SPARQL                                          Evaluation




                                                                                         ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                        Documentation
                                          Ontology Engineering

                    Ontology Life                                  Application
                    Cycle                                          Scenarios
                       Development                                     Search
                                                    Tools
                       Maintenance                                     Integration
                                          Ontology development
                                          Storage, reasoning,
                                           alignment, Web interaction,
                                           interfaces

        Page 9
Content




2.   Enterprise Information Management
     Definition
     Information Value Chain
     Market Growth
     Enterprise Ontologies
     Application Scenarios for Ontologies




                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
      Page 10
Enterprise Information Management
Definition
Enterprise Information Management takes an holistic view on decision relevant
information available within an enterprise.



 Vision


 Strategy


 Governance
                                                                     Enterprise
                                                     Data                            Social
                            BI and                                    Content
                                                 Management                         Software      Master Data




                                                                                                                ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
 Organization            Performance                                Management
                                                      and                              and        Management
                         Management                                 and Search
                                                  Integration                     Collaboration
 Process

 Enabling
 Infrastructure

 Metrics


Source: Gartner 2007, EIM conference 2008, Detecon Research 2008.


            Page 11
Enterprise Information Management.
                  Information Value Chain
                  Positioning of the Enterprise Information Management in the overall software application
                  market.


                                                                                                                 Storage/
                                                                                       Generation                             Processing      Integration      Analysis      Presentation
                                                                                                                 Archiving
                                                                           un-struc-




                                                                                        Web
                                                                             tured




                                                                                                            Enterprise Information Management                Content
                                                                                                                                                             Analytics
                         external




                                                                                         Web 2.0 Sources
                                                     structured structured




                                                                                                                                                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                                                                                           Mashups
                                                                                                                                                             Intelligent
                                                                                       Social Networks                                                        Decision     Portals,
                                                                                                                                                      E
                                                                                                                                           B2B                 Mgmt.       reports ,
                                                                                                                                                      T
                                                                                                                                           Integr.          Fraud          dashboards,
                                                                                                            Master Data Management                    L
                                                                                       ERP, CRM,                                                            Detection      scorecards
                                                                                       SCM                  Data
                                                                                                                                           EAI, SOA          BI / BPM
                         internal




                                                                                                            Warehouse
                                            unstruc-




                                                                                          Email
                                             tured




                                                                                                                                                            Enterprise
                                                                                                                   Enterprise Content Management
                                                                                                                                                             Search


                                            Page 12
For illustrative purposes and without changing the implications the value chain is displayed in a linear form.
Enterprise Information Management.
Market Growth
Information Management software and services are the fastest growing segment in the
software market because they leverage information in core business applications.

                                      Market development                                        Assessment

                       Western European IM software market                              Data integration is a core
                                                                                         enabler for all other IM
                                                         CAGR   BI and                   initiatives.
                                                    4.53B€      performance             BI and Performance
                                             4.10               management               Management are the major
                        11%           3.72                      Data management          topics for IM.
                               3.35                     6.2%    and integration
                                                                                        Competing on analytics has




                                                                                                                          ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                    3.01                                        MDM
            2.70                                                Enterprise content       become a key issue, as
    2.42                                                        management and           innovation leaders use
                                                        16.3%   search                   analytics as a means to profit
                                                                Social software          growth.
                                                        23 %
                                                                and collaboration       More important than the
                                                                                         technical solutions for IM are
                                                        13.7%
                                                                                         vision, strategy, policy,
                                                                                         process and organizational
                                                        15.0%
                                                                                         issues.
   2006 2007 2008 2009 2010 2011 2012
Source: Gartner, April 2008.


             Page 13
Enterprise Information Management.
Ontologies and Enterprise Information Management
Ontologies are at the core of Enterprise Information Management.




                                Master Data
                                                                Data Integration
                                Management




       Structured Information                                                      Social Networks




                                                                                                     ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                          Enterprise Ontology




                 Unstructured
                                                                              Business Analytics
                  Information
                                              Data Governance




       Page 14
Enterprise Information Management.
Classification of Ontologies
Ontologies can be classified according their formality. You might be familiar with some
of these categories.




                                       Thessauri                                       General
                                                                  Formal   Frames       Logical
                                    “narrower term”
                 Catalog/ID                                        is-a  (properties) constraints
                                        relation




                                                                                                                                             ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                               Terms/                  Informal             Formal        Value           Disjointness,
                              glossary                    is-a             instance       Restrs.       Inverse, part-Of
                                                                                                                ...




Source:Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory.
Stanford University. KSL-01-02. 2001.


            Page 15
Enterprise Information Management.
Types of Ontologies
Ontologies can be classified according to their degree of reusability.




            R         Application Domain         Application Domain
            E              Ontology                Task Ontology
            U
            S          Domain Ontology          Domain Task Ontology
            A
                        Core Ontology                Task Ontology
            B




                                                                         ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
            I
                                  Top-Level Ontology
            L
            I
                               General/Common Ontology
            T
            Y
                                Representation Ontology




       Page 16
Enterprise Information Management.
Application Scenarios for Ontologies
Ontologies are a means to enable interoperability between machines. They also facilitate
communication between people providing a shared representation of a domain.

           Neutral Authoring                                     Ontology-based Search

    Bases for application                                     Ontologies provide the
     development as core data                                   structure for the navigation of
     model for all applications.                                the results, support browsing
                                                                and classification.
    Typical use case in AI.
                                                               Ontologies allow for term
                                                                disambiguation and query
                                                                rewriting.




                                                                                                  ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                        Ontologies
    Global view on information.                               Specification of software
                                                                systems and automation of
    Organization and
                                                                code generation.
     management of information
     sources and their interrelation.                          MDA.
    Consistency checking.                                     SOA.
    Currently most relevant use
     case for enterprises.
 Common Access to Information                                Ontology-based Specification
Source: Jasper & Uschold, 1999.
            Page 17
Enterprise Information Management.
Application Scenarios for Ontologies: Common Access to Information
Building a semantic application is feasible. However it still requires deep technology
insight and best practices for system integration are still under development.

                                                           Ontology                Mapping of
      Feasibility              Requirements              development              data sources            Set-up storage            Application

Stakeholders*
   Consultants (few        System                   Technology              Technology              Technicians            System
    consultants              integrators (only         consultants (Few         consultants              (Scalable stores        integrators (user
    available                small players).           consultants,             (Technology              available, good         interfaces
    understanding                                      engineering              requires deep            vendor support).        available,
                            Weakest part in
    the benefit).                                      environment              technical skills).      Integration with        customization
                             the value chain.
                                                       available).                                       all kinds of data       required,
   Decision makers                                                            Technology ready
                                                                                                         bases possible.         expertise not
    (technicians, less                                Staff (requires          for structured da-
                                                                                                                                 available).




                                                                                                                                                     ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
    C-level                                            knowledge                ta sources, not
    awareness) .                                       transfer).               ready for unstruc-
                                                                                tured sources.
Key Deliverables
   Feasibility study.      Detailed                 Ontology.               Mapping of data         Integration of         User interface.
   Identification of        description of                                     sources to               triplestores with
                                                      Sample queries.                                                          Process to
    data stores to be        application.                                       ontology.                data sources by
                                                                                                         means of                support
    integrated.                                       Translation.
                            Evaluation                                        Enterprise                                       application.
                                                                                                         mappings.
   Business case-           criteria.                Logical model.           ontology based
                                                                                                                                SOA
                                                                                SOA messages.           Scalable storage
                            Detailed                 Documentation of                                                          infrastructure.
   Rough                                                                                                solution.
    application              description of            ontology.
                             data sources.                                                              Maintainability.
    architecture.                                     Maintainability of
                            Application               ontology.
                             scenario
                             *Who should do it (current problems)
             Page 18
Content




3.   Ontology Engineering Methodologies
     Historical Background
     Methodologies Related to Knowledge Management Systems
     Methodologies Related to Software Engineering
     Distributed Ontology Engineering
     New Approaches




                                                             ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
     Condensed Version




      Page 19
Ontology Engineering Methodologies
Ontology Engineering Definition
“a comprehensive, integrated series of techniques or methods creating a general
systems theory of how a class of thought-intensive work ought be performed”*

                       Typical Elements of a Methodology              Description

                                                           Application scenario
                                                              The application scenario of a
                                                               methodology describes the
                                                               general settings to which the
                                                               methodology is applicable.
                                   Process                 Process




                                                                                                 ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                              The process describes the
                                                               activities and tasks, including
                                                               their sequence, input and
                                 Application
                                                               output, to be performed by the
                                  scenario
                                                               stakeholders.

                                                  Tools    Roles
                       Roles
                                                              Describe the responsibilities
                                                               and tasks of different
                                                               stakeholders in the process.


*Source: IEEE, 1990.
            Page 20
Ontology Engineering Methodologies
Historical Background
The development of ontology engineering methodologies has a long history and was
strongly influenced by specific project experiences of the authors.




                                                CommonKADS
   Enterprise Ontology                       [Schreiber et al., 1999]
  [Uschold & King, 1995]




                                                                                   ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
              IDEF5
       [Benjamin et al. 1994]
                                                          Holsapple&Joshi
                                                      [Holsapple & Joshi, 2002]
                              CO4
                         [Euzenat, 1995]


        Page 21
Ontology Engineering Methodologies
Methodologies Related to Knowledge Management Systems
The On-To-Knowledge methodology takes a pragmatic approach to ontology engineering
and contains many useful tips to support non-experts to build an ontology.

                    Go /            Sufficient      Meets
                                                                  Roll-out?          Changes?
                                                                                                                         Description
                   No Go?         requirements   requirements
                                        ?              ?
                                                                                                              Application scenario
                  Common         ORSD +            Target        Evaluated       Evolved
                   KADS         Semi-formal       ontology       ontology        ontology                        Suited for developing
                                                                                                   Human
                 Worksheets      ontology
                                description
                                                                                                                  ontologies for knowledge
                                                                                                   Issues
                                                                                                                  management applications.

                                       Refine-          Evalu-
                                                                  Application          Knowledge
     Feasibility
       study
                        Kickoff
                                        ment            ation
                                                                        &              Management             Process




                                                                                                                                                  ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                   Evolution
                                                                                       Application               Sequential/iterative process.

                                                                                                 Software
Identify ..     5. Capture        7. Refine semi- 10. Technology- 13. Apply
                                                                                                Engineering   Roles
1. Problems &     requirements      formal ontology    focussed      ontology
  opportunities   specification in description         evaluation 14. Manage
                  ORSD
                                                                                                                 Ontology engineers design the
2. Focus of KM                    8. Formalize into 11. User-        evolution and
  application   6. Create semi-     target ontology    focussed      maintenance                                  ontology and interact with
3. (OTK-) Tools   formal ontology 9. Create            evaluation                                                 domain experts to develop it.
                  description       Prototype       12. Ontology-
4. People
                                                       focussed
                                                       evaluation


                                  Ontology Development
Source: Sure, 2003.
              Page 22
Ontology Engineering Methodologies
Methodologies Related to Software Engineering
METHONTOLOGY contains the most comprehensive description of ontology engineering
activities. It is targeted at ontology engineers.

 Ontology Management
 Scheduling, controlling, quality assurance
                                                                                                                                                           Description

                                                                                                                                                Application scenario
                                                                           Feasibility study
                                                                           Problems, opportunities, potential solutions, economic feasibility
                                                                                                                                                   Generic methodology for
                                                                                                                                                    ontology development in
                                                                                                                                                    centralized settings.
                                  Knowledge acquisition
                                  Knowledge acquisition




                                                                           Domain analysis
                                                                           motivating scenarios, competency questions, existing solutions
                                                          Ontology reuse
                                                          Ontology reuse
     Documentation




                                                                                                                                                Process
                     Evaluation




                                                                           Conceptualization                                                       Serial process comparable to




                                                                                                                                                                                      ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                           conceptualization of the model, integration and extension of             the waterfall model in software
                                                                           existing solutions
                                                                                                                                                    engineering.

                                                                           Implementation                                                       Roles
                                                                           implementation of the formal model in a representation language
                                                                                                                                                   The ontology is developed by
                                                                                                                                                    ontology engineers.
                                                                           Maintenance
                                                                           adaptation of the ontology according to new requirements
                                                                                                                                                   Users are not directly involved
                                                                                                                                                    in the engineering process.

                                                                           Use
                                                                           ontology based search, integration, negotiation


Source: METHONTOLOGY, Gómez-Pérez, A. ,1996.
                         Page 23
Ontology Engineering Methodologies
Distributed Ontology Engineering
DILIGENT is a methodology for distributed ontology engineering. It focuses on
consensus building aspects through argumentation.

                                                                                          Description
                                          2. Local
                                         Adaptation                            Application scenario
       1. Central                                        O1       3. Central      Generic methodology for
          Build                                                    Analysis        ontology development in
                                   5. Local
                                                                                   decentralized settings.
                                   Update
                                                                               Process
                                   OI          O-User 1                           Rapid prototyping process
 Ontology                                       …




                                                                                                                   ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                                   with short update cycles.
 User Domain          Ontology
                                                                  Board
      Expert          Engineer                                                 Roles
                                                         On                       Users actively participate in
                                              O-User n
                                                                                   the ontology engineering
                                                                                   process.
               Knowledge                                                          Modeling decisions are made
               Engineer                                   4. Central               by a board including ontology
                                                          Revision                 engineers.


Source: DILIGENT: Tempich, 2006.


            Page 24
Ontology Engineering Methodologies
New Approaches
Recent methodologies concentrate on decentralization. They apply Web 2.0 paradigms in
order to facilitate the development of community-driven ontologies.

                       Wikis                     Games                          Tagging

          Employing Wikis in              Usage of games with           Tagging is a very
           ontology engineering             a purpose to motivate          successful approach
           enables easy                     humans to undertake            to organize all sorts of
           participation of the             complex activities in          content on the Web.
           community and lowers             the ontology life cycle.
                                                                          Tags often describe
           barriers of entry for non-      During such a game,            the meaning of the
           experts.                         players describe               tagged content in one
                                            images, text or                term.




                                                                                                      ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
          So far less suitable for
           developing complex,              videos. Players
                                                                          Approaches to derive
           highly axiomatized               receive a higher score
                                                                           formal ontologies from
           ontologies.                      if they describe the
                                                                           tag clouds are
                                            content in the same
                                                                           emerging.
                                            way.




           Ontology engineering increasingly becomes an community activity.
Source: Siorpaes 2008, Braun 2007.
             Page 25
Ontology Engineering Methodologies
Condensed Version
In our project experience we found out that a number of process steps and activities
distinguish ontology development from related engineering efforts. They are crucial for
the success of an ontology development project.



                                                                 Requirements analysis
                                                                 motivating scenarios, use cases, existing solutions,
                                         Knowledge acquisition

                                                                 effort estimation, competency questions, application requirements
                     Test (Evaluation)
   Documentation




                                                                 Glossary creation (Conceptualization)




                                                                                                                                     ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                 conceptualization of the model, integration and extension of
                                                                 existing solutions




                                                                 Modeling (Implementation)
                                                                 implementation of the formal model in a representation language




                   Page 26
Content




4.   Ontology Engineering
     Overview
     Set-up
     Requirements Analysis
     Glossary Creation
     Modeling




                             ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
     Testing




      Page 27
Ontology Engineering
Overview
We present an ontology engineering process consisting of 5 steps. We describe those
aspects of the engineering process which are essential for its successful
implementation.
   Condensed Ontology Engineering Process                                                                                                                      Elements to Be Discussed

                                                                             Requirements analysis
                                                                             motivating scenarios, use cases, existing solutions,
                                                     Knowledge acquisition   effort estimation, competency questions, application requirements
                                 Test (Evaluation)
                 Documentation




                                                                             Glossary (Conceptualization)
                                                                             conceptualization of the model, integration and extension of
                                                                             existing solutions




                                                                             Modeling (Implementation)
                                                                             implementation of the formal model in a representation language




                                                                                                                                                                                                   ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                                                                                                                          Objectives
                                                                                                Req.                                             Glossary
                                                                                              analysis
                                                                                                                                                 creation
                                                                                                                                                                         Process step
     Set-up                                                                                                                 Ontology
                                                                                                                                                            Methods,
                                                                                                                                                                                        Examples
                                                                                                                                                            activities
                                                                                                              Test                                          and tools
                                                                                                                                                 Modeling




       Page 28
Ontology Engineering
Set-up: Objectives
In the set-up phase the project manager organizes the ontology development project and
gets the buy-in of all stakeholders in order to enable a smooth project implementation.

                     Objectives                                                 Input

   Buy-in of all stakeholders (management, project       Management contract to design an ontology.
    managers, business units, developers) to the
                                                          Business objectives and alignment with
    proposed ontology engineering process.
                                                           business strategy.
   The scope of the ontology in terms of domains is
                                                          Business goals and business drivers.
    clear.
   The application scenario for the ontology is
    defined (integration, search, communication,




                                                                                                        ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
    etc).
   The number and types of applications are                                    Output
    defined.
                                                          Defined ontology engineering process.
   The proposed tool chain works smoothly.
                                                          Defined high-level application scenario
                                                          List of stakeholders.
                                                          List of relevant domains to be modeled.
                                                          Operational tool chain.
                                                          Training material.

         Page 29
Ontology Engineering
Set-up: Methods, activities and tools
The set-up step can be completed within one month.


    Activities      Methods                                        Tools

Define objectives Workshops     Ontology requirements specification document (ORSD).
                                Contains information about the goal, domain and scope of the ontology.
                                Specifies design guidelines, naming conventions.
                                Lists knowledge sources, potential users, usage scenarios and supported
                                applications.

Project            Effort       ONTOCOM for development effort estimation, project management tools
management         estimation




                                                                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
Select             Workshops,   Existing standards and ontologies.
information        research
                                e.g., TM Forum defines NGOSS Shared Information/Data Model (SID)
sources and
                                (domain model for the telecommunication industry), Gist (http://gist-
reusable
                                ont.com/) Semantic Arts Inc. (upper ontology), OASIS (standardization
ontologies
                                body)
                                Internal definitions, thesauri, glossaries, hierarchies, domain models.

Set-up tool chain Proof of      Specify requirements for tracking tool, glossary documentation tool,
                  concept       ontology engineering environment, ontology learning tool (if applicable),
                                data integration tool, reasoner, SOA environment, triplestore, enterprise
                                applications (if applicable), representation language.
         Page 30
Ontology Engineering
0. Set-up: Examples
We provide some examples for the selection of the relevant setting or use case.



Be clear about why the ontology is being developed and what its intended usages are.
   Data and process interoperability.
   Systems engineering.
   Semantic search.
   Semantic annotation.
   Communication between people and organizations.




                                                                                             ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
Semantic search.
   Semi-formal ontology.
   Usage of natural language labels and naming conventions.
   Well-balanced at schema and instance level.
   Rich conceptualization.
   Syntactical and semantic correctness.


The ontology should not contain all the possible information about the domain of interest.


         Page 31
Ontology Engineering
Set-up: Examples
Examples of existing reusable ontologies are the TMForum SID domain model and the
GIST upper ontology.

                 SID domain model                     GIST upper ontology




                                                                                    ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
       Page 32
Ontology Engineering
Requirements Analysis: Objectives
In the requirements analysis step the project team collects the expectations from the
stakeholders towards the ontology.

                     Objectives                                              Input

   Guidelines for the modeling phase.                  Scope and application scenario of the ontology.
   Criteria for the evaluation of the engineering      Information sources.
    effort.
                                                        Domain of the ontology.
   Agreement on ontology use cases for the
    ontology between stakeholders.




                                                                                                           ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
   The development of the ontology is pursued
    in monthly cycles.
                                                                            Output
   Although requirements can be collected at
    all times, they should be prioritized.              Competency questions and use case
                                                         descriptions forming the list of requirements.
   We select an excerpt of the total list of
    requirements such that they can be
    implemented and tested within one month.




         Page 33
Ontology Engineering
Requirements Analysis: Methods, activities and tools
Collecting competency questions is a proven method to describe the requirements for
an ontology.

   Activities                                      Methods                                              Tools

Collect           Collaboration, brainstorming.                                                   (Semantic)wiki to
requirements                                                                                      store and describe
                  Competency questions
                                                                                                  requirements.
                        A set of queries which place demands on the underlying ontology.
                        Ontology must be able to represent the questions using its terminology
                         and the answers based on the axioms.
                        Ideally, in a staged manner, where consequent questions require the
                         input from the preceding ones.




                                                                                                                       ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                        A rationale for each competency question should be given.

Discuss and       Argumentation, workshops                                                        DILIGENT
select relevant                                                                                   argumentation
requirements                                                                                      framework.

Align with        Workshops
business                 Collect requirements from the business process owners and align
process                   them with the information needs in the respective process.




        Page 34
Ontology Engineering
Requirements Analysis: Examples
We present examples of requirements produced in this step.



      Competency Questions                   Other Requirements                            Issues

    Which wine characteristics           Concepts in the ontology             An ontology reflects an
     should I consider when                should be bi-lingual.                 abstracted view of a domain of
     choosing a wine?                                                            interest. You should not
                                          The ontology should not have
                                                                                 model all possible views upon
    Is Bordeaux a red or white            more than 10 inheritance
                                                                                 a domain of interest, or to
     wine?                                 levels.
                                                                                 attend to capture all
    Does Cabernet Sauvignon go           The ontology should be                knowledge potentially
     well with seafood?                    extended and maintained by            available about the respective




                                                                                                                   ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                           non-experts.                          domain.
    What is the best choice of
     wine for grilled meat?               The ontology should be used          Even after the scope of the
                                           to build an online restaurant         ontology has been defined,
    Which characteristics of a
                                           guide.                                the number of competency
     wine affect its appropriateness
                                                                                 questions can grow very
     for a dish?                          The ontology should be usable
                                                                                 quickly  modularization,
                                           on an available collection of
    Does a flavor or body of a                                                  prioritization.
                                           restaurant descriptions written
     specific wine change with
                                           in German.                           Requirements are often
     vintage year?
                                                                                 contradictory  prioritization.

Source: Ontology Development 101.
            Page 35
Ontology Engineering
Glossary Creation: Objectives
The glossary is the reference for all further activities. It describes the terms of the
ontology in a comprehensive manner.

                     Objectives                                                 Input

   Define terms of the ontology in natural language.      List of requirements.
   Build up the body of knowledge of the terms
    used in an organization.
   Facilitate communication within the organization.
   Buy-in from all stakeholders in terms of selected
    objects, descriptions and relationships.




                                                                                                           ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
   Alignment of objects with business processes.
                                                                               Output
                                                           Important terms of the domain.
                                                           Descriptions of the terms with examples.
                                                           Usage scenarios of the terms in the process.
                                                           High-level relationships between terms.
                                                           Alignment of glossary terms an business
                                                            processes.

         Page 36
Ontology Engineering
Glossary Creation: Methods, activities and tools
Wiki technology is very suitable to support the creation and documentation of the
glossary, because it enables easy collaboration and access.

             Activities                       Methods                                Tools

Collect glossary terms.             Workshops, collaboration.      (Semantic) wikis to collect terms.

Describe the glossary terms in      (Automatic or semi-            A top-down development process
their application context, list     automatic) Knowledge           starts with the definition of the most
synonyms, list domain               acquisition techniques, e.g.   general concepts in the domain and
assumptions, give examples of       Information Extraction,        subsequent specialization of the
instances of the glossary terms.    Ontology Learning.             concepts.
                                                                   A bottom-up development process




                                                                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
Define hierarchical relationships
                                                                   starts with the definition of the most
between glossary terms.
                                                                   specific classes, the leaves of the
Define domain relationships                                        hierarchy, with subsequent grouping of
among glossary terms.                                              these classes into more general
                                                                   concepts.
                                                                   A middle-out approach: define the more
                                                                   salient concepts first and then
Align business processes with
                                                                   generalize and specialize them
glossary terms.
                                                                   appropriately.



        Page 37
Ontology Engineering
Glossary: Examples
The glossary is the first step towards an axiomatized ontology.


      Collect Glossary Terms                        Hierarchy                        Visualization

    wine, grape, winery, location,      Apple is a subclass of Fruit.                                Top
     wine color, wine body,                  Every apple is a fruit.                                 level
    wine flavor, sugar content,         Red wines is a subclass of
     white wine, red wine,                Wine.
    Bordeaux wine, food, seafood,           Every red wine is a wine.
     fish, meat, vegetables,                                                Middle
                                         Chianti wine is a subclass of
                                                                             level




                                                                                                              ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
    cheese…                              Red wine.
                                             Every Chianti wine is a red
                                              wine.
 and not


    sightseeing Tuscany, atoms
     and molecules of alcohol,
     underage drinking laws…
                                                                                                     Bottom
                                                                                                      level
Source: Ontology Development 101.
            Page 38
Ontology Engineering
Modeling: Objectives

In the modeling step the glossary terms are transferred in the target representation
language.

                      Objectives                                        Input

   Development of a machine understandable      Glossary.
    ontology.
   Development of a reusable ontology.
   Input for the application.




                                                                                                 ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                     Output
                                                 Class descriptions.
                                                 Hierarchy.
                                                 Attributes of each class.
                                                 Associations and other type of relationships
                                                  among classes.
                                                 Restrictions/constraints on classes.

         Page 39
Ontology Engineering
Modeling: Methods, activities and tools
The complexity of the modeling step depends on the representation language and on
the complexity of the requirements.
              Activities                          Methods                                 Tools

Define classes, their attributes and Depending on the representation       e.g., Protégé, Ontoprise’
relationships.                       language different modeling           OntoStudio, TopQuadrant’s
                                     primitives are available:             Topbraid Composer, Altova,
                                                                           OntologyWorks, IBM, tools for
                                              Cardinality, domain and
                                                                           thesaurus or taxonomy building.
                                               range restrictions.
                                              Hierarchies of
                                               relationships.




                                                                                                               ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                              Inverse, functional,
                                               transitive relationships.
                                              Equivalence.
                                              Disjoint classes.

Define and apply modeling             Reusing modeling patterns.           Collections of patterns available
patterns.                                                                  from software engineering and
                                                                           modeling.

Integrate with existing application   Ontology alignment and mapping.      Open sources prototypes
environment.                                                               available.

Relate to upper ontology.             Upper ontology.                      Consistency checking, ontology
         Page 40
                                                                           alignment tools.
Ontology Engineering
Modeling: Examples
Ontology development is supported by a variety of tools. Besides OWL and RDFS, UML
is gaining increasing attention as an ontology modeling language.

                     Ontology in UML                                 Guidelines
                                                            There is no unique way to
                                                             model a domain correctly —
                                                             there are always viable
                                                             alternatives. The best solution
                                                             always depends on the
                                                             application that you have in
                                                             mind and the extensions that
                                                             you anticipate.




                                                                                               ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                            Ontology development is
                                                             necessarily an iterative
                                                             process.
                                                            Concepts in the ontology
                                                             should be close to objects
                                                             (physical or logical) and
                                                             relationships in your domain
                                                             of interest. These are most
                                                             likely to be nouns (objects) or
                                                             verbs (relationships) in
                                                             sentences that describe your
                                                             domain.


       Page 41
Ontology Engineering
Modeling: Examples
The entity specification pattern allows to add characteristics to an entity without
changing the model. Useful if large numbers of attributes need to be represented.

                           Entity Specification Characteristic/Entity Characteristic Pattern


          0..n                                           0..1                   0..n
                         EntitySpecification                                                                Entity
                                          E.g Mobile    Entity SpecificationDescribes
                               0..n                                                                     1
                        EntitySpecCharacterizedBy
EntitySpecDescribedBy          0..n
                                                         0..1 Entity SpeciCharacteristicDescribes
                      EntitySpecCharacteristic
                                          E.g Color                                                 Entity DefineBy
                                1
                     EntitySpecCharEnumeratedBy
                               0..n                                                     0..n            0..n
          0..n                                           0..1                    0..n
                 EntitySpecCharacteristicvalue                                                 EntityCharacteristicvalue
                                E.g Chocolate, red, …                                                                 E.g Chocolate
                                                        Entity SpeciCharValueDescribes



Source: TMForum, SID.


           Page 42
Ontology Engineering
Modeling: Examples
The business interaction pattern facilitates the representation of e.g., the communication
with a client in a business context.

                                                                Business Interaction

                                                               BusinessInteractionType
                                                                           1

                                                             BusinessInteractionTypeCategorize
                                                                          0..n
                                                                                                              BusinessInteractionRelationship
                       BusinessInteractionInvolvesLocation        BusinessInteraction            0..n
     Place       0..n                                    0..n
                                                                                                        BusinessInteractionReferences
                                                                                                 0..n
                          BusinessInteractionLocation
                                                                      1

                                                                          BusinessInteractionInvolves
                                                                   0..n
                                                               BusinessInteractionRole



                       PartyRole                             ResourceInteractionRole                            CustomerAccount
                                                                                                                 InteractionRole
Source: TMForum, SID.


             Page 43
Ontology Engineering
Test: Objectives
The test step should ensure that the result of the modeling phase does indeed meet the
requirements set in the requirements analysis phase

                       Objectives                                      Input

   Tested ontology.                              Modeled ontology.
   Running proof-of concept.                     Requirements.
   Satisfaction of the stakeholders.
   Demonstration to top management that the
    approach works.
   Early possibility to adapt approach.




                                                                                         ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                       Output
                                                  Refined and tested ontology.




         Page 44
Ontology Engineering
Test: Methods, activities and tools

In the test phase the stakeholders get a direct feedback if their effort has been
successful.

             Activities                         Methods                     Tools

Test queries and consistency      Unit tests.                 Often supported by ontology
checking.                                                     engineering environment.

Deploy ontology in proof-of-      Proof-of-concept.           -
concept set-up.

Run different test corresponding to Test methods known from   Tools used in Software
the requirements.                   Software Development.     Development.




                                                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
        Page 45
Content




5.   Useful Methods
     OntoCom – Effort Estimation for Ontology Engineering
     Modeling Guidelines
     Argumentation




                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
      Page 46
Content




5.   Useful Management and Support Methods
     Ontocom – Effort Estimation for Ontology Engineering
     Modeling Guidelines
     Argumentation




                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
      Page 47
Ontocom
Management Summary
Ontocom is a framework to help you estimate the effort related to the building of an
ontology. It make accurate predictions and can be improved with data from your team.

                 Elements                                        Description
                                               Ontocom is a framework to estimate the effort
                                                related to ontology development.
                                               Ontocom comes with
                                                     A process for effort estimation.
                                                     A formula and a tool calculating the
                                                      estimations. and




                                                                                                ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                  Process                            A methodology to adjust the estimations
                                                      to a particular company.
                                               Ontocom takes the size, the domain, the
                 Ontocom                        development complexity, the expected
                                                quality and the experience of the staff as
                                                input factors.
       Formula              Methodology        Ontocom estimates ontology development costs
                                                with a 30% accuracy in 80% of the cases.




       Page 48
Ontocom
Process
Applying Ontocom is easy and follows a five step process. The project manager defines
the different parameters based on the process guidelines which are part of the framework.




                                    Evaluation of
                    Evaluation of                   Evaluation of
     Size                               the                         Evaluation of




                                                                                                        ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                     the domain                     the expected                    Effort estimation
  estimation                        development                     the personnel
                     complexity                        quality
                                     complexity




          Page 49
Ontocom
Formula
The formula uses information collected in the ontology development process and of
historical information collected from previous projects to make the effort estimation.

                             Parametric Effort Estimation Method




             PM = A * (Size ) * ∏ CD i                   B




                                                                                         ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
             Person     Normaliza-         Size of the                      Cost
             Month      tion Factor        Ontology                        Drivers


                                                    Learning
                                                     Factor
       Result
       Input from project manager
       Input from methodology

          Page 50
Ontocom
Formula: Example
The parameters associated with the different cost drivers are predefined in our
calculation tool.

                                 Effort Estimation Formula


       Person             Size of the                              Cost
       Month              Ontology                                Drivers


                                               Quality of personnel   Development complexity




                                                                                               ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                    very high             very high

       6.9 PM      =     500 Entities   *           high              X   high

                                                X   average               average

                                                    low                   low

                                                    very low              very low




       Page 51
OntoCom
Methodology
For a high accuracy of the model we calculated the parameters aggregating the
experience of over 40 ontology engineering projects. And counting.


                                        Model generation

            Data collection                  Data analysis                         Model Usage


                                         Model calibration

  Specify cost       Collect data   Analyze data       Calibrate         Evaluate                            Release
    drivers                                             model             model                               model




                                                                                                                              ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                                                    Effort estimations

                                                                      12.000
                                                                      11.000
                                                                      10.000
                                                                       9.000               +/ -30% tolerance
                                                                       8.000
                                                                                                        average
                                                                       7.000                           estimation
                                                                       6.000
                                                                       5.000
                                                                       4.000
                                                                       3.000
                                                                       2.000
                                                                       1.000
                                                                          0
                                                                               0   4   8      1    1     2     2    2   3 3
                                                                                              2    6     0     4    8   2 4




The accuracy of the model increases if it is adapted and calibrated with data from your own business.


          Page 52
OntoCom
Process: Cost Drivers
Step 1: Size of the ontology



                      Explanation                                               Guidelines
The size of the ontology. This includes all first class       Determining the size of a prospected ontology is
citizens of an ontology. Size is measured in kilo              a challenging task in an early stage of the
entities.                                                      ontology development process.
   All class definitions.                                    Existing domain ontologies can help to get a
   All attribute definitions.                                 rough capture.
   All relationship definitions.                         1.   Search for existing domain ontologies.
   All rule definitions.




                                                                                                                  ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                          2.   Compare coverage of existing domain
                                                               ontologies with the required level of detail.
                       Examples
                                                          3.   Calculate expected size of the new ontology.
An ontology has
   500 classes.
   700 attributes.
   300 relations.
   no rules.
This totals in 1.5 k entities.

         Page 53
OntoCom
Process: Cost Drivers
Step 2: Evaluation of the domain



                    Explanation                                           Guidelines
The Domain Analysis Complexity accounts for           DOMAIN
those features of the application setting which
                                                         Very Low: narrow scope, common-sense
influence the complexity of the engineering
                                                          knowledge, low connectivity.
outcomes. It consist of three sub categories:
   The domain complexity.                               Very High: wide scope, expert knowledge, high
                                                          connectivity.
   The requirements complexity.
                                                      REQUIREMENTS
   The available information sources.




                                                                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                         Very Low few, simple requirements.
                     Examples                            Very High: very high number of req. with a high
                                                          conflicting degree, high number of usability
   An ontology for the cooking domain, having a          requirements.
    low number of requirements and a high number
    of available information sources has a very low   INFORMATION SOURCES
    to low domain complexity.                            Very Low high number of sources in various
   An ontology for the chemistry domain, with a          forms.
    high number of requirements and a low number         Very High none.
    of available information sources has a high to
    very high domain complexity.

         Page 54
OntoCom
Process: Cost Drivers
Step 3: Evaluation of the development complexity



                    Explanation                                            Guidelines
   The Conceptualization Complexity accounts          CONCEPTUALIZATION
    for the impact of a complex conceptual model
                                                          Very Low: concept list.
    on the overall costs.
                                                          Very High: instances, no patterns, considerable
   The Implementation Complexity takes into
                                                           number of constraints.
    consideration the additional efforts arisen from
    the usage of a specific implementation
    language.
                                                       IMPLEMENTATION




                                                                                                             ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                                                          Low: The semantics of the conceptualization
                     Examples                              compatible to the one of the implementation
                                                           language.
   An ontology for a search application with an
    thesaurus has a low development complexity.           High: Major differences between the two.

   An ontology for the chemistry domain,
    modeling reaction patterns has a high
    development complexity.




        Page 55
OntoCom
Process: Cost Drivers
Step 4: Evaluation of expected quality



                    Explanation                                              Guidelines
   The Evaluation Complexity accounts for the          ONTOLOGY EVALUATION
    additional efforts eventually invested in
                                                           Very Low: small number of tests, easily
    generating test cases and evaluating test
                                                            generated and reviewed.
    results. This includes the effort to document the
    ontology.                                              Very High: extensive testing, difficult to
                                                            generate and review.
   Required reusability to capture the additional
    effort associated with the development of a         REUSEABILITY




                                                                                                              ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
    reusable ontology,
                                                           Very Low: Ontology is used for this application
                                                            only.
                      Examples
                                                           Very High: Ontology should be used across
   An ontology which is used for one application           many applications as an upper level ontology.
    only without extensive testing has a low factor.
   An integration ontology which should be used
    across an entire organization or for many web
    users with high documentation requirements has
    a high or very high factor.



         Page 56
OntoCom
Process: Cost Drivers
Step 5: Evaluation of personnel



                    Explanation                                             Guidelines
   Ontologist/Domain Expert Capability accounts        ONTOLOGIST/DOMAIN EXPERT CAPABILITY
    for the perceived ability and efficiency of the
                                                           Very Low: 15%.
    single actors involved in the process (ontologist
    and domain expert) as well as their teamwork           Very High: 95%.
    capabilities.
                                                        ONTOLOGYIST/DOMAIN EXPERT EXPERIENCE
   Ontologist/Domain Expert Experience to mea-
                                                           Very Low: 2 month (ontology) / 6 month
    sure the level of experience of the engineering
                                                            (domain).




                                                                                                      ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
    team w.r.t. performing ontology engineering.
                                                           Very High: 3 years (ontology) / 7 years
                     Examples                               (domain).

   The new project member who has never worked
    with ontologies nor has any experience with the
    domain has a very low expert experience.
   The project manager who has been working with
    ontologies for several years and is experienced
    in a certain field has a very high expert
    experience.


         Page 57
OntoCom
Case Study: Estimated vs. Actual Figures
The actual effort was higher than expected. This is mainly due to frequent changes in the
modeling team and to technical problems aligning the process and ontology model.

                                     Actual Effort                                              Evaluation

                                                                           Changes in the development team:
                  12.000
                                                                              The team consisted of in average 4 people.
                  11.000
                  10.000                                                      The team structure changed quite often due to
                   9.000                                                       management decisions.
                                                                Entities
no. of entities




                   8.000                                                      This required experienced modelers to train
                   7.000                                                       newcomers.




                                                                                                                                  ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
                   6.000                                                   Aligning the process model with the ontology:
                   5.000                                                      Tool support to define the data objects required
                   4.000                                                       for activities in a process model is limited.
                   3.000                                                      The original model does not account for the
                   2.000                                                       integration of an ontology with a process model.
                   1.000                                                   Size
                       0
                                                                              The estimate of the size of the ontology is
                           0     5    10    15   20   25   30     35           relatively good.
                                           person month
                                                                              The project is ongoing.

                       Page 58
Content




5.   Useful Management and Support Methods
     Ontocom – Effort Estimation for Ontology Engineering
     Modeling Guidelines
     Argumentation




                                                            ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
      Page 59
Modeling Guidelines
Definition

Ontologies are conceptual models. Modeling guidelines developed for semantic models
apply to ontologies as well. Ontologies can capture domain or use case knowledge.

         Conceptual/semantic models                                     Domain/use case models
    A conceptual/semantic model is a mental                     A domain model is a conceptual model of
     model which captures ideas in a domain of                    a system which describes the various
     interest in terms of modeling primitives.                    entities involved in the system and the
                                                                  relationships among them.
    The aim of conceptual model is to express
     the meaning of terms and concepts used                      The domain model is created to capture
     by domain experts to discuss the problem,                    the key concepts and the vocabulary of
     and to find the correct relationships                        the system.
     between different concepts.
                                                                 It identifies the relationships among all




                                                                                                                 ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
    The conceptual model attempts to clarify                     major entities within the system, as well
     the meaning of various usually                               as their main methods and attributes.
     ambiguous terms, and ensure that             Influence
                                                                 In this way the model provides a
     problems with different interpretations of
                                                                  structural view of the system which is
     the terms and concepts cannot occur.
                                                                  normally complemented by the dynamic
    Once the domain of interest has been                         views in use case models.
     modeled, the model becomes a stable
                                                                 The aim of a domain model is to verify and
     basis for subsequent development of
                                                                  validate the understanding of a domain of
     applications in the domain.
                                                                  interest among various stakeholders of the
    A conceptual model can be described                          project group. It is especially helpful as a
     using various notations.                                     communication tool and a focusing point
                                                                  between technical and business teams.

          Page 60
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008
Ontology engineering ESTC2008

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Ontology engineering ESTC2008

  • 1. Ontology Engineering Tutorial Dr. Elena Simperl Dr. Christoph Tempich 24.09.2008
  • 2. Ontology Engineering Presenters Dr. Elena Simperl Dr. Christoph Tempich I am working as a senior researcher in the areas of I am a management consultant in the CP semantic systems and technologies at STI Information Technology. at Detecon International. Innsbruck, University of Innsbruck. Sectors Sectors  Telecommunication, Automotive.  ICT. Functions Functions  Enterprise Information Management. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  R&D.  Technology markets and innovation.  Education and training. Experience Experience  Consulting at Detecon International and  Vice director STI Innsbruck, STI International Bearingpoint (KPMG). service coordinator for education.  More than 10 years Semantic Web research.  8 years of experience in Semantic Web research  Workshops and more than 40 publications. and development  Two Innovation Awards for Semantic Web  Management of more than 10 national and EU applications. projects Page 1
  • 3. Ontology Engineering Management Summary You will learn how to convince your CEO to start an ontology engineering initiative and how to implement it successfully in your company.  The core objective of Information Management is to enable informed decision making.  Ontologies play an increasing role in holistically organizing enterprise information. In the tutorial we will position Ontology Engineering in the broader context of Enterprise Information Management. We will introduce the five steps - setup, requirements analysis, glossary creation, modeling, test - of our methodology for developing ontologies in an enterprise context. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  For each step we will present the roles of participating actors, the methods and software available to guide and even partially automatize particular tasks, and metrics which can be used to assess the quality of the intermediary outcomes.  In addition we will discuss best practices and guidelines related to critical aspects of Ontology Engineering:  Modeling specific types of knowledge.  Resolving conflicts in collaborative ontology building processes through argumentation.  The automatic generation and learning of ontologies from existing unstructured data sources.  Ontology engineering economics. Page 2
  • 4. Content 1. Motivation 2. Enterprise Information Management 3. Ontology Engineering Methodologies 4. Ontology Development ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 5. Useful Management and Support Methods 6. Conclusion Page 3
  • 5. Content 1. Motivation Vision Customer Challenges and Benefits Ontology Engineering Definition What Do You Expect from This Tutorial? Tutorial Overview ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 4
  • 6. Motivation Vision Seamless data integration across different data sources on the Web is a great challenge. It also promises huge business opportunities and cost savings. Web-scale data integration Description  Semantic technologies refer to techniques to help a computer program automatically process and use arbitrary data (and services) in a meaningful way.  After a decade of intensive ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT research semantic technologies seem to be the best candidate to offer the underlying technology for Web-scale data integration.  Ontologies are a core enabler of this vision.  Relevant terms: Web 2.0, Web 3.0, Semantic Web Services, Semantic Web. Page 5
  • 7. Motivation Customer Challenges and Benefits Enterprise Information Management aims to handle the rapidly growing amounts of information relevant to an enterprise business. Challenges Benefits  The amount of available and potentially relevant  A flexible information infrastructure which allows information is growing exponentially. to manage the growing amount of information.  The time for decision making is decreasing  Flexible integration with business processes to continuously. account for changing business requirements.  Decision making is based on multiple highly  Up-to-date information for accurate decision heterogeneous, distributed and rapidly changing making. information sources. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Fulfillment of regulatory requirements.  The explosion of information is facilitated by  Timely and informed interaction with customers technical developments such as RFID , email, responding to their needs. Web, Internet.  Identification of information leakage, customer  Enterprises need an intelligent information demands and cost drivers. infrastructure which delivers the right information at the right place in the right time.  Data Governance is required for clear responsibilities. Page 6
  • 8. Motivation Ontology Engineering Definition “the set of activities that concern the ontology development process, the ontology life cycle, and the methodologies, tools and languages for building ontologies”.* Methodologies:  Distributed  Centralized Ontology Languages Development  OWL Process  RDF(S)  Requirements  SPARQL  Evaluation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Documentation Ontology Engineering Ontology Life Application Cycle: Scenarios  Development  Search Tools  Maintenance  Integration  Ontology development  Storage, reasoning, alignment, Web interaction, interfaces *Source: Gómez-Pérez, A. et. al.: Ontological Engineering. Advanced Information and Knowledge Processing. Springer, 2003. Page 7
  • 9. Motivation What Do You Expect from This Tutorial? Please tell us ... Examples Content Personal Situation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Presenters Format Objectives Interaction Page 8
  • 10. Motivation Tutorial Overview In this tutorial we focus on the ontology development process and introduce methodologies, applications scenarios and tools. Methodologies:  Distributed  Centralized Ontology Languages Development  OWL Process  RDF(S)  Requirements  SPARQL  Evaluation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Documentation Ontology Engineering Ontology Life Application Cycle Scenarios  Development  Search Tools  Maintenance  Integration  Ontology development  Storage, reasoning, alignment, Web interaction, interfaces Page 9
  • 11. Content 2. Enterprise Information Management Definition Information Value Chain Market Growth Enterprise Ontologies Application Scenarios for Ontologies ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 10
  • 12. Enterprise Information Management Definition Enterprise Information Management takes an holistic view on decision relevant information available within an enterprise. Vision Strategy Governance Enterprise Data Social BI and Content Management Software Master Data ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Organization Performance Management and and Management Management and Search Integration Collaboration Process Enabling Infrastructure Metrics Source: Gartner 2007, EIM conference 2008, Detecon Research 2008. Page 11
  • 13. Enterprise Information Management. Information Value Chain Positioning of the Enterprise Information Management in the overall software application market. Storage/ Generation Processing Integration Analysis Presentation Archiving un-struc- Web tured Enterprise Information Management Content Analytics external Web 2.0 Sources structured structured ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Mashups Intelligent Social Networks Decision Portals, E B2B Mgmt. reports , T Integr. Fraud dashboards, Master Data Management L ERP, CRM, Detection scorecards SCM Data EAI, SOA BI / BPM internal Warehouse unstruc- Email tured Enterprise Enterprise Content Management Search Page 12 For illustrative purposes and without changing the implications the value chain is displayed in a linear form.
  • 14. Enterprise Information Management. Market Growth Information Management software and services are the fastest growing segment in the software market because they leverage information in core business applications. Market development Assessment Western European IM software market  Data integration is a core enabler for all other IM CAGR BI and initiatives. 4.53B€ performance  BI and Performance 4.10 management Management are the major 11% 3.72 Data management topics for IM. 3.35 6.2% and integration  Competing on analytics has ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 3.01 MDM 2.70 Enterprise content become a key issue, as 2.42 management and innovation leaders use 16.3% search analytics as a means to profit Social software growth. 23 % and collaboration  More important than the technical solutions for IM are 13.7% vision, strategy, policy, process and organizational 15.0% issues. 2006 2007 2008 2009 2010 2011 2012 Source: Gartner, April 2008. Page 13
  • 15. Enterprise Information Management. Ontologies and Enterprise Information Management Ontologies are at the core of Enterprise Information Management. Master Data Data Integration Management Structured Information Social Networks ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Enterprise Ontology Unstructured Business Analytics Information Data Governance Page 14
  • 16. Enterprise Information Management. Classification of Ontologies Ontologies can be classified according their formality. You might be familiar with some of these categories. Thessauri General Formal Frames Logical “narrower term” Catalog/ID is-a (properties) constraints relation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Terms/ Informal Formal Value Disjointness, glossary is-a instance Restrs. Inverse, part-Of ... Source:Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001. Page 15
  • 17. Enterprise Information Management. Types of Ontologies Ontologies can be classified according to their degree of reusability. R Application Domain Application Domain E Ontology Task Ontology U S Domain Ontology Domain Task Ontology A Core Ontology Task Ontology B ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT I Top-Level Ontology L I General/Common Ontology T Y Representation Ontology Page 16
  • 18. Enterprise Information Management. Application Scenarios for Ontologies Ontologies are a means to enable interoperability between machines. They also facilitate communication between people providing a shared representation of a domain. Neutral Authoring Ontology-based Search  Bases for application  Ontologies provide the development as core data structure for the navigation of model for all applications. the results, support browsing and classification.  Typical use case in AI.  Ontologies allow for term disambiguation and query rewriting. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Ontologies  Global view on information.  Specification of software systems and automation of  Organization and code generation. management of information sources and their interrelation.  MDA.  Consistency checking.  SOA.  Currently most relevant use case for enterprises. Common Access to Information Ontology-based Specification Source: Jasper & Uschold, 1999. Page 17
  • 19. Enterprise Information Management. Application Scenarios for Ontologies: Common Access to Information Building a semantic application is feasible. However it still requires deep technology insight and best practices for system integration are still under development. Ontology Mapping of Feasibility Requirements development data sources Set-up storage Application Stakeholders*  Consultants (few  System  Technology  Technology  Technicians  System consultants integrators (only consultants (Few consultants (Scalable stores integrators (user available small players). consultants, (Technology available, good interfaces understanding engineering requires deep vendor support). available,  Weakest part in the benefit). environment technical skills).  Integration with customization the value chain. available). all kinds of data required,  Decision makers  Technology ready bases possible. expertise not (technicians, less  Staff (requires for structured da- available). ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT C-level knowledge ta sources, not awareness) . transfer). ready for unstruc- tured sources. Key Deliverables  Feasibility study.  Detailed  Ontology.  Mapping of data  Integration of  User interface.  Identification of description of sources to triplestores with  Sample queries.  Process to data stores to be application. ontology. data sources by means of support integrated.  Translation.  Evaluation  Enterprise application. mappings.  Business case- criteria.  Logical model. ontology based  SOA SOA messages.  Scalable storage  Detailed  Documentation of infrastructure.  Rough solution. application description of ontology. data sources.  Maintainability. architecture.  Maintainability of  Application ontology. scenario *Who should do it (current problems) Page 18
  • 20. Content 3. Ontology Engineering Methodologies Historical Background Methodologies Related to Knowledge Management Systems Methodologies Related to Software Engineering Distributed Ontology Engineering New Approaches ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Condensed Version Page 19
  • 21. Ontology Engineering Methodologies Ontology Engineering Definition “a comprehensive, integrated series of techniques or methods creating a general systems theory of how a class of thought-intensive work ought be performed”* Typical Elements of a Methodology Description Application scenario  The application scenario of a methodology describes the general settings to which the methodology is applicable. Process Process ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  The process describes the activities and tasks, including their sequence, input and Application output, to be performed by the scenario stakeholders. Tools Roles Roles  Describe the responsibilities and tasks of different stakeholders in the process. *Source: IEEE, 1990. Page 20
  • 22. Ontology Engineering Methodologies Historical Background The development of ontology engineering methodologies has a long history and was strongly influenced by specific project experiences of the authors. CommonKADS Enterprise Ontology [Schreiber et al., 1999] [Uschold & King, 1995] ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT IDEF5 [Benjamin et al. 1994] Holsapple&Joshi [Holsapple & Joshi, 2002] CO4 [Euzenat, 1995] Page 21
  • 23. Ontology Engineering Methodologies Methodologies Related to Knowledge Management Systems The On-To-Knowledge methodology takes a pragmatic approach to ontology engineering and contains many useful tips to support non-experts to build an ontology. Go / Sufficient Meets Roll-out? Changes? Description No Go? requirements requirements ? ? Application scenario Common ORSD + Target Evaluated Evolved KADS Semi-formal ontology ontology ontology  Suited for developing Human Worksheets ontology description ontologies for knowledge Issues management applications. Refine- Evalu- Application Knowledge Feasibility study Kickoff ment ation & Management Process ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Evolution Application  Sequential/iterative process. Software Identify .. 5. Capture 7. Refine semi- 10. Technology- 13. Apply Engineering Roles 1. Problems & requirements formal ontology focussed ontology opportunities specification in description evaluation 14. Manage ORSD  Ontology engineers design the 2. Focus of KM 8. Formalize into 11. User- evolution and application 6. Create semi- target ontology focussed maintenance ontology and interact with 3. (OTK-) Tools formal ontology 9. Create evaluation domain experts to develop it. description Prototype 12. Ontology- 4. People focussed evaluation Ontology Development Source: Sure, 2003. Page 22
  • 24. Ontology Engineering Methodologies Methodologies Related to Software Engineering METHONTOLOGY contains the most comprehensive description of ontology engineering activities. It is targeted at ontology engineers. Ontology Management Scheduling, controlling, quality assurance Description Application scenario Feasibility study Problems, opportunities, potential solutions, economic feasibility  Generic methodology for ontology development in centralized settings. Knowledge acquisition Knowledge acquisition Domain analysis motivating scenarios, competency questions, existing solutions Ontology reuse Ontology reuse Documentation Process Evaluation Conceptualization  Serial process comparable to ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT conceptualization of the model, integration and extension of the waterfall model in software existing solutions engineering. Implementation Roles implementation of the formal model in a representation language  The ontology is developed by ontology engineers. Maintenance adaptation of the ontology according to new requirements  Users are not directly involved in the engineering process. Use ontology based search, integration, negotiation Source: METHONTOLOGY, Gómez-Pérez, A. ,1996. Page 23
  • 25. Ontology Engineering Methodologies Distributed Ontology Engineering DILIGENT is a methodology for distributed ontology engineering. It focuses on consensus building aspects through argumentation. Description 2. Local Adaptation Application scenario 1. Central O1 3. Central  Generic methodology for Build Analysis ontology development in 5. Local decentralized settings. Update Process OI O-User 1  Rapid prototyping process Ontology … ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT with short update cycles. User Domain Ontology Board Expert Engineer Roles On  Users actively participate in O-User n the ontology engineering process. Knowledge  Modeling decisions are made Engineer 4. Central by a board including ontology Revision engineers. Source: DILIGENT: Tempich, 2006. Page 24
  • 26. Ontology Engineering Methodologies New Approaches Recent methodologies concentrate on decentralization. They apply Web 2.0 paradigms in order to facilitate the development of community-driven ontologies. Wikis Games Tagging  Employing Wikis in  Usage of games with  Tagging is a very ontology engineering a purpose to motivate successful approach enables easy humans to undertake to organize all sorts of participation of the complex activities in content on the Web. community and lowers the ontology life cycle.  Tags often describe barriers of entry for non-  During such a game, the meaning of the experts. players describe tagged content in one images, text or term. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  So far less suitable for developing complex, videos. Players  Approaches to derive highly axiomatized receive a higher score formal ontologies from ontologies. if they describe the tag clouds are content in the same emerging. way. Ontology engineering increasingly becomes an community activity. Source: Siorpaes 2008, Braun 2007. Page 25
  • 27. Ontology Engineering Methodologies Condensed Version In our project experience we found out that a number of process steps and activities distinguish ontology development from related engineering efforts. They are crucial for the success of an ontology development project. Requirements analysis motivating scenarios, use cases, existing solutions, Knowledge acquisition effort estimation, competency questions, application requirements Test (Evaluation) Documentation Glossary creation (Conceptualization) ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT conceptualization of the model, integration and extension of existing solutions Modeling (Implementation) implementation of the formal model in a representation language Page 26
  • 28. Content 4. Ontology Engineering Overview Set-up Requirements Analysis Glossary Creation Modeling ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Testing Page 27
  • 29. Ontology Engineering Overview We present an ontology engineering process consisting of 5 steps. We describe those aspects of the engineering process which are essential for its successful implementation. Condensed Ontology Engineering Process Elements to Be Discussed Requirements analysis motivating scenarios, use cases, existing solutions, Knowledge acquisition effort estimation, competency questions, application requirements Test (Evaluation) Documentation Glossary (Conceptualization) conceptualization of the model, integration and extension of existing solutions Modeling (Implementation) implementation of the formal model in a representation language ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Objectives Req. Glossary analysis creation Process step Set-up Ontology Methods, Examples activities Test and tools Modeling Page 28
  • 30. Ontology Engineering Set-up: Objectives In the set-up phase the project manager organizes the ontology development project and gets the buy-in of all stakeholders in order to enable a smooth project implementation. Objectives Input  Buy-in of all stakeholders (management, project  Management contract to design an ontology. managers, business units, developers) to the  Business objectives and alignment with proposed ontology engineering process. business strategy.  The scope of the ontology in terms of domains is  Business goals and business drivers. clear.  The application scenario for the ontology is defined (integration, search, communication, ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT etc).  The number and types of applications are Output defined.  Defined ontology engineering process.  The proposed tool chain works smoothly.  Defined high-level application scenario  List of stakeholders.  List of relevant domains to be modeled.  Operational tool chain.  Training material. Page 29
  • 31. Ontology Engineering Set-up: Methods, activities and tools The set-up step can be completed within one month. Activities Methods Tools Define objectives Workshops Ontology requirements specification document (ORSD). Contains information about the goal, domain and scope of the ontology. Specifies design guidelines, naming conventions. Lists knowledge sources, potential users, usage scenarios and supported applications. Project Effort ONTOCOM for development effort estimation, project management tools management estimation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Select Workshops, Existing standards and ontologies. information research e.g., TM Forum defines NGOSS Shared Information/Data Model (SID) sources and (domain model for the telecommunication industry), Gist (http://gist- reusable ont.com/) Semantic Arts Inc. (upper ontology), OASIS (standardization ontologies body) Internal definitions, thesauri, glossaries, hierarchies, domain models. Set-up tool chain Proof of Specify requirements for tracking tool, glossary documentation tool, concept ontology engineering environment, ontology learning tool (if applicable), data integration tool, reasoner, SOA environment, triplestore, enterprise applications (if applicable), representation language. Page 30
  • 32. Ontology Engineering 0. Set-up: Examples We provide some examples for the selection of the relevant setting or use case. Be clear about why the ontology is being developed and what its intended usages are.  Data and process interoperability.  Systems engineering.  Semantic search.  Semantic annotation.  Communication between people and organizations. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Semantic search.  Semi-formal ontology.  Usage of natural language labels and naming conventions.  Well-balanced at schema and instance level.  Rich conceptualization.  Syntactical and semantic correctness. The ontology should not contain all the possible information about the domain of interest. Page 31
  • 33. Ontology Engineering Set-up: Examples Examples of existing reusable ontologies are the TMForum SID domain model and the GIST upper ontology. SID domain model GIST upper ontology ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 32
  • 34. Ontology Engineering Requirements Analysis: Objectives In the requirements analysis step the project team collects the expectations from the stakeholders towards the ontology. Objectives Input  Guidelines for the modeling phase.  Scope and application scenario of the ontology.  Criteria for the evaluation of the engineering  Information sources. effort.  Domain of the ontology.  Agreement on ontology use cases for the ontology between stakeholders. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  The development of the ontology is pursued in monthly cycles. Output  Although requirements can be collected at all times, they should be prioritized.  Competency questions and use case descriptions forming the list of requirements.  We select an excerpt of the total list of requirements such that they can be implemented and tested within one month. Page 33
  • 35. Ontology Engineering Requirements Analysis: Methods, activities and tools Collecting competency questions is a proven method to describe the requirements for an ontology. Activities Methods Tools Collect Collaboration, brainstorming. (Semantic)wiki to requirements store and describe Competency questions requirements.  A set of queries which place demands on the underlying ontology.  Ontology must be able to represent the questions using its terminology and the answers based on the axioms.  Ideally, in a staged manner, where consequent questions require the input from the preceding ones. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  A rationale for each competency question should be given. Discuss and Argumentation, workshops DILIGENT select relevant argumentation requirements framework. Align with Workshops business  Collect requirements from the business process owners and align process them with the information needs in the respective process. Page 34
  • 36. Ontology Engineering Requirements Analysis: Examples We present examples of requirements produced in this step. Competency Questions Other Requirements Issues  Which wine characteristics  Concepts in the ontology  An ontology reflects an should I consider when should be bi-lingual. abstracted view of a domain of choosing a wine? interest. You should not  The ontology should not have model all possible views upon  Is Bordeaux a red or white more than 10 inheritance a domain of interest, or to wine? levels. attend to capture all  Does Cabernet Sauvignon go  The ontology should be knowledge potentially well with seafood? extended and maintained by available about the respective ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT non-experts. domain.  What is the best choice of wine for grilled meat?  The ontology should be used  Even after the scope of the to build an online restaurant ontology has been defined,  Which characteristics of a guide. the number of competency wine affect its appropriateness questions can grow very for a dish?  The ontology should be usable quickly  modularization, on an available collection of  Does a flavor or body of a prioritization. restaurant descriptions written specific wine change with in German.  Requirements are often vintage year? contradictory  prioritization. Source: Ontology Development 101. Page 35
  • 37. Ontology Engineering Glossary Creation: Objectives The glossary is the reference for all further activities. It describes the terms of the ontology in a comprehensive manner. Objectives Input  Define terms of the ontology in natural language.  List of requirements.  Build up the body of knowledge of the terms used in an organization.  Facilitate communication within the organization.  Buy-in from all stakeholders in terms of selected objects, descriptions and relationships. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Alignment of objects with business processes. Output  Important terms of the domain.  Descriptions of the terms with examples.  Usage scenarios of the terms in the process.  High-level relationships between terms.  Alignment of glossary terms an business processes. Page 36
  • 38. Ontology Engineering Glossary Creation: Methods, activities and tools Wiki technology is very suitable to support the creation and documentation of the glossary, because it enables easy collaboration and access. Activities Methods Tools Collect glossary terms. Workshops, collaboration. (Semantic) wikis to collect terms. Describe the glossary terms in (Automatic or semi- A top-down development process their application context, list automatic) Knowledge starts with the definition of the most synonyms, list domain acquisition techniques, e.g. general concepts in the domain and assumptions, give examples of Information Extraction, subsequent specialization of the instances of the glossary terms. Ontology Learning. concepts. A bottom-up development process ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Define hierarchical relationships starts with the definition of the most between glossary terms. specific classes, the leaves of the Define domain relationships hierarchy, with subsequent grouping of among glossary terms. these classes into more general concepts. A middle-out approach: define the more salient concepts first and then Align business processes with generalize and specialize them glossary terms. appropriately. Page 37
  • 39. Ontology Engineering Glossary: Examples The glossary is the first step towards an axiomatized ontology. Collect Glossary Terms Hierarchy Visualization  wine, grape, winery, location,  Apple is a subclass of Fruit. Top wine color, wine body,  Every apple is a fruit. level  wine flavor, sugar content,  Red wines is a subclass of white wine, red wine, Wine.  Bordeaux wine, food, seafood,  Every red wine is a wine. fish, meat, vegetables, Middle  Chianti wine is a subclass of level ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  cheese… Red wine.  Every Chianti wine is a red wine. and not  sightseeing Tuscany, atoms and molecules of alcohol, underage drinking laws… Bottom level Source: Ontology Development 101. Page 38
  • 40. Ontology Engineering Modeling: Objectives In the modeling step the glossary terms are transferred in the target representation language. Objectives Input  Development of a machine understandable  Glossary. ontology.  Development of a reusable ontology.  Input for the application. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Output  Class descriptions.  Hierarchy.  Attributes of each class.  Associations and other type of relationships among classes.  Restrictions/constraints on classes. Page 39
  • 41. Ontology Engineering Modeling: Methods, activities and tools The complexity of the modeling step depends on the representation language and on the complexity of the requirements. Activities Methods Tools Define classes, their attributes and Depending on the representation e.g., Protégé, Ontoprise’ relationships. language different modeling OntoStudio, TopQuadrant’s primitives are available: Topbraid Composer, Altova, OntologyWorks, IBM, tools for  Cardinality, domain and thesaurus or taxonomy building. range restrictions.  Hierarchies of relationships. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Inverse, functional, transitive relationships.  Equivalence.  Disjoint classes. Define and apply modeling Reusing modeling patterns. Collections of patterns available patterns. from software engineering and modeling. Integrate with existing application Ontology alignment and mapping. Open sources prototypes environment. available. Relate to upper ontology. Upper ontology. Consistency checking, ontology Page 40 alignment tools.
  • 42. Ontology Engineering Modeling: Examples Ontology development is supported by a variety of tools. Besides OWL and RDFS, UML is gaining increasing attention as an ontology modeling language. Ontology in UML Guidelines  There is no unique way to model a domain correctly — there are always viable alternatives. The best solution always depends on the application that you have in mind and the extensions that you anticipate. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Ontology development is necessarily an iterative process.  Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain. Page 41
  • 43. Ontology Engineering Modeling: Examples The entity specification pattern allows to add characteristics to an entity without changing the model. Useful if large numbers of attributes need to be represented. Entity Specification Characteristic/Entity Characteristic Pattern 0..n 0..1 0..n EntitySpecification Entity E.g Mobile Entity SpecificationDescribes 0..n 1 EntitySpecCharacterizedBy EntitySpecDescribedBy 0..n 0..1 Entity SpeciCharacteristicDescribes EntitySpecCharacteristic E.g Color Entity DefineBy 1 EntitySpecCharEnumeratedBy 0..n 0..n 0..n 0..n 0..1 0..n EntitySpecCharacteristicvalue EntityCharacteristicvalue E.g Chocolate, red, … E.g Chocolate Entity SpeciCharValueDescribes Source: TMForum, SID. Page 42
  • 44. Ontology Engineering Modeling: Examples The business interaction pattern facilitates the representation of e.g., the communication with a client in a business context. Business Interaction BusinessInteractionType 1 BusinessInteractionTypeCategorize 0..n BusinessInteractionRelationship BusinessInteractionInvolvesLocation BusinessInteraction 0..n Place 0..n 0..n BusinessInteractionReferences 0..n BusinessInteractionLocation 1 BusinessInteractionInvolves 0..n BusinessInteractionRole PartyRole ResourceInteractionRole CustomerAccount InteractionRole Source: TMForum, SID. Page 43
  • 45. Ontology Engineering Test: Objectives The test step should ensure that the result of the modeling phase does indeed meet the requirements set in the requirements analysis phase Objectives Input  Tested ontology.  Modeled ontology.  Running proof-of concept.  Requirements.  Satisfaction of the stakeholders.  Demonstration to top management that the approach works.  Early possibility to adapt approach. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Output  Refined and tested ontology. Page 44
  • 46. Ontology Engineering Test: Methods, activities and tools In the test phase the stakeholders get a direct feedback if their effort has been successful. Activities Methods Tools Test queries and consistency Unit tests. Often supported by ontology checking. engineering environment. Deploy ontology in proof-of- Proof-of-concept. - concept set-up. Run different test corresponding to Test methods known from Tools used in Software the requirements. Software Development. Development. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 45
  • 47. Content 5. Useful Methods OntoCom – Effort Estimation for Ontology Engineering Modeling Guidelines Argumentation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 46
  • 48. Content 5. Useful Management and Support Methods Ontocom – Effort Estimation for Ontology Engineering Modeling Guidelines Argumentation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 47
  • 49. Ontocom Management Summary Ontocom is a framework to help you estimate the effort related to the building of an ontology. It make accurate predictions and can be improved with data from your team. Elements Description  Ontocom is a framework to estimate the effort related to ontology development.  Ontocom comes with  A process for effort estimation.  A formula and a tool calculating the estimations. and ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Process  A methodology to adjust the estimations to a particular company.  Ontocom takes the size, the domain, the Ontocom development complexity, the expected quality and the experience of the staff as input factors. Formula Methodology  Ontocom estimates ontology development costs with a 30% accuracy in 80% of the cases. Page 48
  • 50. Ontocom Process Applying Ontocom is easy and follows a five step process. The project manager defines the different parameters based on the process guidelines which are part of the framework. Evaluation of Evaluation of Evaluation of Size the Evaluation of ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT the domain the expected Effort estimation estimation development the personnel complexity quality complexity Page 49
  • 51. Ontocom Formula The formula uses information collected in the ontology development process and of historical information collected from previous projects to make the effort estimation. Parametric Effort Estimation Method PM = A * (Size ) * ∏ CD i B ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Person Normaliza- Size of the Cost Month tion Factor Ontology Drivers Learning Factor Result Input from project manager Input from methodology Page 50
  • 52. Ontocom Formula: Example The parameters associated with the different cost drivers are predefined in our calculation tool. Effort Estimation Formula Person Size of the Cost Month Ontology Drivers Quality of personnel Development complexity ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT very high very high 6.9 PM = 500 Entities * high X high X average average low low very low very low Page 51
  • 53. OntoCom Methodology For a high accuracy of the model we calculated the parameters aggregating the experience of over 40 ontology engineering projects. And counting. Model generation Data collection Data analysis Model Usage Model calibration Specify cost Collect data Analyze data Calibrate Evaluate Release drivers model model model ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Effort estimations 12.000 11.000 10.000 9.000 +/ -30% tolerance 8.000 average 7.000 estimation 6.000 5.000 4.000 3.000 2.000 1.000 0 0 4 8 1 1 2 2 2 3 3 2 6 0 4 8 2 4 The accuracy of the model increases if it is adapted and calibrated with data from your own business. Page 52
  • 54. OntoCom Process: Cost Drivers Step 1: Size of the ontology Explanation Guidelines The size of the ontology. This includes all first class  Determining the size of a prospected ontology is citizens of an ontology. Size is measured in kilo a challenging task in an early stage of the entities. ontology development process.  All class definitions.  Existing domain ontologies can help to get a  All attribute definitions. rough capture.  All relationship definitions. 1. Search for existing domain ontologies.  All rule definitions. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 2. Compare coverage of existing domain ontologies with the required level of detail. Examples 3. Calculate expected size of the new ontology. An ontology has  500 classes.  700 attributes.  300 relations.  no rules. This totals in 1.5 k entities. Page 53
  • 55. OntoCom Process: Cost Drivers Step 2: Evaluation of the domain Explanation Guidelines The Domain Analysis Complexity accounts for DOMAIN those features of the application setting which  Very Low: narrow scope, common-sense influence the complexity of the engineering knowledge, low connectivity. outcomes. It consist of three sub categories:  The domain complexity.  Very High: wide scope, expert knowledge, high connectivity.  The requirements complexity. REQUIREMENTS  The available information sources. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Very Low few, simple requirements. Examples  Very High: very high number of req. with a high conflicting degree, high number of usability  An ontology for the cooking domain, having a requirements. low number of requirements and a high number of available information sources has a very low INFORMATION SOURCES to low domain complexity.  Very Low high number of sources in various  An ontology for the chemistry domain, with a forms. high number of requirements and a low number  Very High none. of available information sources has a high to very high domain complexity. Page 54
  • 56. OntoCom Process: Cost Drivers Step 3: Evaluation of the development complexity Explanation Guidelines  The Conceptualization Complexity accounts CONCEPTUALIZATION for the impact of a complex conceptual model  Very Low: concept list. on the overall costs.  Very High: instances, no patterns, considerable  The Implementation Complexity takes into number of constraints. consideration the additional efforts arisen from the usage of a specific implementation language. IMPLEMENTATION ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Low: The semantics of the conceptualization Examples compatible to the one of the implementation language.  An ontology for a search application with an thesaurus has a low development complexity.  High: Major differences between the two.  An ontology for the chemistry domain, modeling reaction patterns has a high development complexity. Page 55
  • 57. OntoCom Process: Cost Drivers Step 4: Evaluation of expected quality Explanation Guidelines  The Evaluation Complexity accounts for the ONTOLOGY EVALUATION additional efforts eventually invested in  Very Low: small number of tests, easily generating test cases and evaluating test generated and reviewed. results. This includes the effort to document the ontology.  Very High: extensive testing, difficult to generate and review.  Required reusability to capture the additional effort associated with the development of a REUSEABILITY ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT reusable ontology,  Very Low: Ontology is used for this application only. Examples  Very High: Ontology should be used across  An ontology which is used for one application many applications as an upper level ontology. only without extensive testing has a low factor.  An integration ontology which should be used across an entire organization or for many web users with high documentation requirements has a high or very high factor. Page 56
  • 58. OntoCom Process: Cost Drivers Step 5: Evaluation of personnel Explanation Guidelines  Ontologist/Domain Expert Capability accounts ONTOLOGIST/DOMAIN EXPERT CAPABILITY for the perceived ability and efficiency of the  Very Low: 15%. single actors involved in the process (ontologist and domain expert) as well as their teamwork  Very High: 95%. capabilities. ONTOLOGYIST/DOMAIN EXPERT EXPERIENCE  Ontologist/Domain Expert Experience to mea-  Very Low: 2 month (ontology) / 6 month sure the level of experience of the engineering (domain). ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT team w.r.t. performing ontology engineering.  Very High: 3 years (ontology) / 7 years Examples (domain).  The new project member who has never worked with ontologies nor has any experience with the domain has a very low expert experience.  The project manager who has been working with ontologies for several years and is experienced in a certain field has a very high expert experience. Page 57
  • 59. OntoCom Case Study: Estimated vs. Actual Figures The actual effort was higher than expected. This is mainly due to frequent changes in the modeling team and to technical problems aligning the process and ontology model. Actual Effort Evaluation Changes in the development team: 12.000  The team consisted of in average 4 people. 11.000 10.000  The team structure changed quite often due to 9.000 management decisions. Entities no. of entities 8.000  This required experienced modelers to train 7.000 newcomers. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 6.000 Aligning the process model with the ontology: 5.000  Tool support to define the data objects required 4.000 for activities in a process model is limited. 3.000  The original model does not account for the 2.000 integration of an ontology with a process model. 1.000 Size 0  The estimate of the size of the ontology is 0 5 10 15 20 25 30 35 relatively good. person month  The project is ongoing. Page 58
  • 60. Content 5. Useful Management and Support Methods Ontocom – Effort Estimation for Ontology Engineering Modeling Guidelines Argumentation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 59
  • 61. Modeling Guidelines Definition Ontologies are conceptual models. Modeling guidelines developed for semantic models apply to ontologies as well. Ontologies can capture domain or use case knowledge. Conceptual/semantic models Domain/use case models  A conceptual/semantic model is a mental  A domain model is a conceptual model of model which captures ideas in a domain of a system which describes the various interest in terms of modeling primitives. entities involved in the system and the relationships among them.  The aim of conceptual model is to express the meaning of terms and concepts used  The domain model is created to capture by domain experts to discuss the problem, the key concepts and the vocabulary of and to find the correct relationships the system. between different concepts.  It identifies the relationships among all ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  The conceptual model attempts to clarify major entities within the system, as well the meaning of various usually as their main methods and attributes. ambiguous terms, and ensure that Influence  In this way the model provides a problems with different interpretations of structural view of the system which is the terms and concepts cannot occur. normally complemented by the dynamic  Once the domain of interest has been views in use case models. modeled, the model becomes a stable  The aim of a domain model is to verify and basis for subsequent development of validate the understanding of a domain of applications in the domain. interest among various stakeholders of the  A conceptual model can be described project group. It is especially helpful as a using various notations. communication tool and a focusing point between technical and business teams. Page 60