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
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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.
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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.
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4. Content
1. Motivation
2. Enterprise Information Management
3. Ontology Engineering Methodologies
4. Ontology Development
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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
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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
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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.
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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.
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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
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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
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11. Content
2. Enterprise Information Management
Definition
Information Value Chain
Market Growth
Enterprise Ontologies
Application Scenarios for Ontologies
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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
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Organization Performance Management
and and Management
Management and Search
Integration Collaboration
Process
Enabling
Infrastructure
Metrics
Source: Gartner 2007, EIM conference 2008, Detecon Research 2008.
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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
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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
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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
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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.
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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
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Enterprise Ontology
Unstructured
Business Analytics
Information
Data Governance
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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
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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.
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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
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I
Top-Level Ontology
L
I
General/Common Ontology
T
Y
Representation Ontology
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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.
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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.
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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).
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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)
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20. Content
3. Ontology Engineering Methodologies
Historical Background
Methodologies Related to Knowledge Management Systems
Methodologies Related to Software Engineering
Distributed Ontology Engineering
New Approaches
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Condensed Version
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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
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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.
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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]
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IDEF5
[Benjamin et al. 1994]
Holsapple&Joshi
[Holsapple & Joshi, 2002]
CO4
[Euzenat, 1995]
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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
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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.
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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
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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.
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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 …
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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.
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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.
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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.
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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)
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conceptualization of the model, integration and extension of
existing solutions
Modeling (Implementation)
implementation of the formal model in a representation language
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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
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Objectives
Req. Glossary
analysis
creation
Process step
Set-up Ontology
Methods,
Examples
activities
Test and tools
Modeling
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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,
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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.
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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
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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
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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.
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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
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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.
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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.
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Output
Class descriptions.
Hierarchy.
Attributes of each class.
Associations and other type of relationships
among classes.
Restrictions/constraints on classes.
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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.
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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
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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.
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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.
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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.
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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.
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Output
Refined and tested ontology.
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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.
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48. Content
5. Useful Management and Support Methods
Ontocom – Effort Estimation for Ontology Engineering
Modeling Guidelines
Argumentation
ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
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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.
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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
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the domain the expected Effort estimation
estimation development the personnel
complexity quality
complexity
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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
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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
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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.
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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.
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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.
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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.
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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
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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.
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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
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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.
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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.
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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.
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60. Content
5. Useful Management and Support Methods
Ontocom – Effort Estimation for Ontology Engineering
Modeling Guidelines
Argumentation
ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
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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.
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