ontology engineering estc2008
DESCRIPTION
Tutorial at the European Semantic Technology Conference ESTC 2008 on ontology engineering.TRANSCRIPT
Ontology Engineering
Tutorial
Dr. Elena Simperl
Dr. Christoph Tempich
24.09.2008
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PresentersOntology Engineering
I am a management consultant in the CP Information Technology. at Detecon International.
Sectors
Telecommunication, Automotive.
Functions
Enterprise Information Management.
Technology markets and innovation.
Experience
Consulting at Detecon International and Bearingpoint (KPMG).
More than 10 years Semantic Web research.
Workshops and more than 40 publications.
Two Innovation Awards for Semantic Web applications.
Dr. Christoph Tempich
I am working as a senior researcher in the areas of semantic systems and technologies at STI Innsbruck, University of Innsbruck.
Sectors
ICT.
Functions
R&D.
Education and training.
Experience
Vice director STI Innsbruck, STI International service coordinator for education.
8 years of experience in Semantic Web research and development
Management of more than 10 national and EU projects
Dr. Elena Simperl
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You will learn how to convince your CEO to start an ontology engineering initiative and how to implement it successfully in your company.
Management SummaryOntology Engineering
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. 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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Content
1. Motivation
2. Enterprise Information Management
3. Ontology Engineering Methodologies
4. Ontology Development
5. Useful Management and Support Methods
6. Conclusion
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Content
1. MotivationVision
Customer Challenges and Benefits
Ontology Engineering Definition
What Do You Expect from This Tutorial?
Tutorial Overview
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VisionSeamless data integration across different data sources on the Web is a great challenge. It also promises huge business opportunities and cost savings.
Motivation
Web-scale data integration
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 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.
Description
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Customer Challenges and BenefitsEnterprise Information Management aims to handle the rapidly growing amounts of information relevant to an enterprise business.
Motivation
A flexible information infrastructure which allows to manage the growing amount of information.
Flexible integration with business processes to account for changing business requirements.
Up-to-date information for accurate decision making.
Fulfillment of regulatory requirements.
Timely and informed interaction with customers responding to their needs.
Identification of information leakage, customer demands and cost drivers.
Benefits
The amount of available and potentially relevant information is growing exponentially.
The time for decision making is decreasing continuously.
Decision making is based on multiple highly heterogeneous, distributed and rapidly changing information sources.
The explosion of information is facilitated by technical developments such as RFID , email, Web, Internet.
Enterprises need an intelligent information infrastructure which delivers the right information at the right place in the right time.
Data Governance is required for clear responsibilities.
Challenges
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*Source: Gómez-Pérez, A. et. al.: Ontological Engineering. Advanced Information and Knowledge Processing. Springer, 2003.
Ontology Engineering Definition“the set of activities that concern the ontology development process, the ontologylife cycle, and the methodologies, tools and languages for building ontologies”.*
Motivation
Methodologies:
Distributed
Centralized
Ontology Engineering
Tools Ontology development
Storage, reasoning, alignment, Web interaction, interfaces
Ontology Life Cycle:
Development
Maintenance
Application Scenarios Search
Integration
Languages OWL
RDF(S)
SPARQL
Ontology Development Process Requirements
Evaluation
Documentation
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Please tell us ...
Presenters
Examples
Interaction
Personal Situation
ObjectivesFormat
Content
MotivationWhat Do You Expect from This Tutorial?
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Tutorial OverviewIn this tutorial we focus on the ontology development process and introduce methodologies, applications scenarios and tools.
Motivation
Methodologies:
Distributed
Centralized
Ontology Engineering
Tools Ontology development
Storage, reasoning, alignment, Web interaction, interfaces
Ontology Life Cycle Development
Maintenance
Application Scenarios Search
Integration
Languages OWL
RDF(S)
SPARQL
Ontology Development Process Requirements
Evaluation
Documentation
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Content
2. Enterprise Information ManagementDefinition
Information Value Chain
Market Growth
Enterprise Ontologies
Application Scenarios for Ontologies
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DefinitionEnterprise Information Management takes an holistic view on decision relevant information available within an enterprise.
Enterprise Information Management
Source: Gartner 2007, EIM conference 2008, Detecon Research 2008.
Governance
OrganizationBI and
Performance Management
Master DataManagement
Enterprise Content
Management and Search
Vision
Process
Metrics
Data Management
and Integration
Social Software
and Collaboration
Enabling Infrastructure
Strategy
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Positioning of the Enterprise Information Management in the overall software application market.
Enterprise Information Management
Social Networks
Mashups
Enterprise Content Management
Enterprise Information Management.
Generation Storage/ Archiving Processing Integration
Intelligent Decision Mgmt.
Analysis Presentation
Master Data Management
Enterprise Search
For illustrative purposes and without changing the implications the value chain is displayed in a linear form.
BI / BPM
Content Analytics
Web
Data Warehousest
ruct
ured
stru
ctur
edun
stru
c-tu
red
un-s
truc
-tu
red
exte
rnal
ERP, CRM, SCM
Web 2.0 Sources
Fraud Detection
Information Value Chain
inte
rnal
B2B Integr.
EAI, SOA
ETL
Portals, reports , dashboards, scorecards
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2.42
2006
2.70
2007
3.01
2008
3.35
2009
3.72
2010
4.10
2011
6.2%
16.3%
23 %
13.7%
15.0%
4.53B€
2012
BI and performance managementData managementand integrationMDMEnterprise content management and searchSocial software and collaboration
11%
Enterprise Information Management.
Western European IM software market
CAGR
Market GrowthInformation Management software and services are the fastest growing segment in the software market because they leverage information in core business applications.
AssessmentMarket development
Data integration is a core enabler for all other IM initiatives.
BI and Performance Management are the major topics for IM.
Competing on analytics has become a key issue, as innovation leaders use analytics as a means to profit growth.
More important than the technical solutions for IM are vision, strategy, policy, process and organizational issues.
Source: Gartner, April 2008.
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Enterprise Information Management.Ontologies and Enterprise Information ManagementOntologies are at the core of Enterprise Information Management.
Enterprise Ontology
Master Data Management
Data Governance
Structured Information
Unstructured Information
Social Networks
Business Analytics
Data Integration
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Enterprise Information Management.Classification of OntologiesOntologies can be classified according their formality. You might be familiar with some of these categories.
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.
Catalog/ID
Thessauri “narrower term”
relationFormal
is-aFrames
(properties)
General Logical
constraints
Terms/ glossary
Informal is-a
Formal instance
Value Restrs.
Disjointness, Inverse, part-Of
...
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Enterprise Information Management.Types of OntologiesOntologies can be classified according to their degree of reusability.
Representation Ontology
General/Common Ontology
Top-Level Ontology
Application Domain Ontology
Application Domain Task Ontology
Domain Ontology Domain Task Ontology
REUSABILITY
Core Ontology Task Ontology
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Enterprise Information Management.Application Scenarios for OntologiesOntologies are a means to enable interoperability between machines. They also facilitate communication between people providing a shared representation of a domain.
Ontologies
Ontology-based Search
Ontologies provide the structure for the navigation of the results, support browsing and classification.
Ontologies allow for term disambiguation and query rewriting.
Ontology-based Specification
Specification of software systems and automation of code generation.
MDA.
SOA.
Common Access to Information
Global view on information.
Organization and management of information sources and their interrelation.
Consistency checking.
Currently most relevant use case for enterprises.
Neutral Authoring
Bases for application development as core data model for all applications.
Typical use case in AI.
Source: Jasper & Uschold, 1999.
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Application Scenarios for Ontologies: Common Access to InformationBuilding a semantic application is feasible. However it still requires deep technology insight and best practices for system integration are still under development.
Enterprise Information Management.
*Who should do it (current problems)
ApplicationFeasibility Set-up storageMapping of data sources
Ontology developmentRequirements
Stakeholders*
Consultants (few consultants available understanding the benefit).
Decision makers (technicians, less C-level awareness) .
System integrators (only small players).
Weakest part in the value chain.
Technology consultants (Few consultants, engineering environment available).
Staff (requires knowledge transfer).
Technology consultants (Technology requires deep technical skills).
Technology ready for structured da-ta sources, not ready for unstruc-tured sources.
Technicians (Scalable stores available, good vendor support).
Integration with all kinds of data bases possible.
System integrators (user interfaces available, customization required, expertise not available).
Key Deliverables Feasibility study.
Identification of data stores to be integrated.
Business case-
Rough application architecture.
Detailed description of application.
Evaluation criteria.
Detailed description of data sources.
Application scenario
Ontology.
Sample queries.
Translation.
Logical model.
Documentation of ontology.
Maintainability of ontology.
Mapping of data sources to ontology.
Enterprise ontology based SOA messages.
Integration of triplestores with data sources by means of mappings.
Scalable storage solution.
Maintainability.
User interface.
Process to support application.
SOA infrastructure.
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Content
3. Ontology Engineering MethodologiesHistorical Background
Methodologies Related to Knowledge Management Systems
Methodologies Related to Software Engineering
Distributed Ontology Engineering
New Approaches
Condensed Version
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*Source: IEEE, 1990.
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”*
Ontology Engineering Methodologies
Typical Elements of a Methodology
Application scenario
The application scenario of a methodology describes the general settings to which the methodology is applicable.
Process
The process describes the activities and tasks, including their sequence, input and output, to be performed by the stakeholders.
Roles
Describe the responsibilities and tasks of different stakeholders in the process.
Description
ToolsRoles
Application scenario
Process
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Historical BackgroundOntology Engineering Methodologies
The development of ontology engineering methodologies has a long history and was strongly influenced by specific project experiences of the authors.
Enterprise Ontology[Uschold & King, 1995]
IDEF5[Benjamin et al. 1994]
CO4[Euzenat, 1995]
CommonKADS[Schreiber et al., 1999]
Holsapple&Joshi[Holsapple & Joshi, 2002]
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Methodologies Related to Knowledge Management SystemsOntology Engineering Methodologies
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.
10. Technology-focussedevaluation
11. User-focussedevaluation
12. Ontology-focussedevaluation
KickoffRefine-ment
Evalu-ation
Application&
Evolution
5. Capturerequirementsspecification in ORSD
6. Create semi-formal ontology description
7. Refine semi-formal ontology description
8. Formalize intotarget ontology
9. CreatePrototype
13. Applyontology
14. Manage evolution and maintenance
Feasibilitystudy
Identify ..1. Problems &
opportunities2. Focus of KM
application3. (OTK-) Tools4. People
ORSD + Semi-formal
ontology description
Targetontology
Evaluatedontology
Common KADS
Worksheets
Go /No Go?
Ontology Development
Sufficientrequirements
?
Meetsrequirements
?Roll-out? Changes?
Evolvedontology
Knowledge Management Application
HumanIssues
SoftwareEngineering
10. Technology-focussedevaluation
11. User-focussedevaluation
12. Ontology-focussedevaluation
KickoffRefine-ment
Evalu-ation
Application&
Evolution
5. Capturerequirementsspecification in ORSD
6. Create semi-formal ontology description
7. Refine semi-formal ontology description
8. Formalize intotarget ontology
9. CreatePrototype
13. Applyontology
14. Manage evolution and maintenance
Feasibilitystudy
Identify ..1. Problems &
opportunities2. Focus of KM
application3. (OTK-) Tools4. People
ORSD + Semi-formal
ontology description
Targetontology
Evaluatedontology
Common KADS
Worksheets
Go /No Go?
Ontology Development
Sufficientrequirements
?
Meetsrequirements
?Roll-out? Changes?
Evolvedontology
Knowledge Management Application
HumanIssues
SoftwareEngineering
Description
Application scenario
Suited for developing ontologies for knowledge management applications.
Process
Sequential/iterative process.
Roles
Ontology engineers design the ontology and interact with domain experts to develop it.
Source: Sure, 2003.
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Methodologies Related to Software EngineeringOntology Engineering Methodologies
METHONTOLOGY contains the most comprehensive description of ontology engineering activities. It is targeted at ontology engineers.
Ontology ManagementScheduling, controlling, quality assurance
Domain analysismotivating scenarios, competency questions, existing solutions
Conceptualizationconceptualization of the model, integration and extension of existing solutions
Implementationimplementation of the formal model in a representation language
Maintenanceadaptation of the ontology according to new requirements
Ontology reuse
Evaluation
Docum
entation
Useontology based search, integration, negotiation
Feasibility studyProblems, opportunities, potential solutions, economic feasibility
Know
ledge acquisition
Ontology ManagementScheduling, controlling, quality assuranceOntology ManagementScheduling, controlling, quality assurance
Domain analysismotivating scenarios, competency questions, existing solutions
Conceptualizationconceptualization of the model, integration and extension of existing solutions
Implementationimplementation of the formal model in a representation language
Maintenanceadaptation of the ontology according to new requirements
Ontology reuse
Evaluation
Docum
entation
Useontology based search, integration, negotiation
Feasibility studyProblems, opportunities, potential solutions, economic feasibility
Know
ledge acquisition
Source: METHONTOLOGY, Gómez-Pérez, A. ,1996.
Application scenario
Generic methodology for ontology development in centralized settings.
Process
Serial process comparable to the waterfall model in software engineering.
Roles
The ontology is developed by ontology engineers.
Users are not directly involved in the engineering process.
Description
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Distributed Ontology EngineeringOntology Engineering Methodologies
DILIGENT is a methodology for distributed ontology engineering. It focuses on consensus building aspects through argumentation.
DomainExpert
KnowledgeEngineer
OntologyEngineer
OI
Board
O1
On
O-User 1
O-User n
…OntologyUser
1. Central Build
3. Central Analysis
4. CentralRevision
2. LocalAdaptation
5. LocalUpdate
Application scenario
Generic methodology for ontology development in decentralized settings.
Process
Rapid prototyping process with short update cycles.
Roles
Users actively participate in the ontology engineering process.
Modeling decisions are made by a board including ontology engineers.
Description
Source: DILIGENT: Tempich, 2006.
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New ApproachesOntology Engineering Methodologies
Recent methodologies concentrate on decentralization. They apply Web 2.0 paradigms in order to facilitate the development of community-driven ontologies.
Employing Wikis in ontology engineering enables easy participation of the community and lowers barriers of entry for non-experts.
So far less suitable for developing complex, highly axiomatized ontologies.
Usage of games with a purpose to motivate humans to undertake complex activities in the ontology life cycle.
During such a game, players describe images, text or videos. Players receive a higher score if they describe the content in the same way.
Tagging is a very successful approach to organize all sorts of content on the Web.
Tags often describe the meaning of the tagged content in one term.
Approaches to derive formal ontologies from tag clouds are emerging.
Ontology engineering increasingly becomes an community activity.
Wikis Games Tagging
Source: Siorpaes 2008, Braun 2007.
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Condensed VersionOntology Engineering Methodologies
Requirements analysismotivating scenarios, use cases, existing solutions, effort estimation, competency questions, application requirements
Glossary creation (Conceptualization)conceptualization of the model, integration and extension of existing solutions
Modeling (Implementation)implementation of the formal model in a representation language
Know
ledge acquisition
Test (Evaluation)
Docum
entation
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.
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Content
4. Ontology EngineeringOverview
Set-up
Requirements Analysis
Glossary Creation
Modeling
Testing
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Ontology EngineeringOverviewWe present an ontology engineering process consisting of 5 steps. We describe those aspects of the engineering process which are essential for its successful implementation.
Set-up
Req. analysis
Ontology
Glossary
creation
ModelingTest
ExamplesMethods, activities and tools
Process step
Objectives
Elements to Be DiscussedCondensed Ontology Engineering Process
Requirements analysismotivating scenarios, use cases, existing solutions, effort estimation, competency questions, application requirements
Glossary (Conceptualization)conceptualization of the model, integration and extension of existing solutions
Modeling (Implementation)implementation of the formal model in a representation language
Know
ledge acquisition
Test (Evaluation)
Docum
entation
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Ontology EngineeringSet-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.
Defined ontology engineering process.
Defined high-level application scenario
List of stakeholders.
List of relevant domains to be modeled.
Operational tool chain.
Training material.
Output
Management contract to design an ontology.
Business objectives and alignment with business strategy.
Business goals and business drivers.
Input
Buy-in of all stakeholders (management, project managers, business units, developers) to the proposed ontology engineering process.
The scope of the ontology in terms of domains is clear.
The application scenario for the ontology is defined (integration, search, communication, etc).
The number and types of applications are defined.
The proposed tool chain works smoothly.
Objectives
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Ontology EngineeringSet-up: Methods, activities and toolsThe 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 management
Effort estimation
ONTOCOM for development effort estimation, project management tools
Select information sources and reusable ontologies
Workshops, research
Existing standards and ontologies.
e.g., TM Forum defines NGOSS Shared Information/Data Model (SID) (domain model for the telecommunication industry), Gist (http://gist-ont.com/) Semantic Arts Inc. (upper ontology), OASIS (standardization body)
Internal definitions, thesauri, glossaries, hierarchies, domain models.
Set-up tool chain Proof of concept
Specify requirements for tracking tool, glossary documentation tool, 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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Ontology Engineering0. Set-up: ExamplesWe 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.
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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Ontology EngineeringSet-up: Examples
Examples of existing reusable ontologies are the TMForum SID domain model and the GIST upper ontology.
GIST upper ontologySID domain model
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Ontology EngineeringRequirements Analysis: Objectives
In the requirements analysis step the project team collects the expectations from the stakeholders towards the ontology.
Competency questions and use case descriptions forming the list of requirements.
Output
Scope and application scenario of the ontology.
Information sources.
Domain of the ontology.
Input
Guidelines for the modeling phase.
Criteria for the evaluation of the engineering effort.
Agreement on ontology use cases for the ontology between stakeholders.
The development of the ontology is pursued in monthly cycles.
Although requirements can be collected at all times, they should be prioritized.
We select an excerpt of the total list of requirements such that they can be implemented and tested within one month.
Objectives
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Ontology EngineeringRequirements Analysis: Methods, activities and tools
Collecting competency questions is a proven method to describe the requirements for an ontology.
Activities Methods Tools
Collect requirements
Collaboration, brainstorming.
Competency questions 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.
A rationale for each competency question should be given.
(Semantic)wiki to store and describe requirements.
Discuss and select relevant requirements
Argumentation, workshops DILIGENT argumentation framework.
Align with business process
Workshops Collect requirements from the business process owners and align
them with the information needs in the respective process.
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Ontology EngineeringRequirements Analysis: ExamplesWe present examples of requirements produced in this step.
Concepts in the ontology should be bi-lingual.
The ontology should not have more than 10 inheritance levels.
The ontology should be extended and maintained by non-experts.
The ontology should be used to build an online restaurant guide.
The ontology should be usable on an available collection of restaurant descriptions written in German.
Other Requirements
Which wine characteristics should I consider when choosing a wine?
Is Bordeaux a red or white wine?
Does Cabernet Sauvignon go well with seafood?
What is the best choice of wine for grilled meat?
Which characteristics of a wine affect its appropriateness for a dish?
Does a flavor or body of a specific wine change with vintage year?
Competency Questions
An ontology reflects an abstracted view of a domain of interest. You should not model all possible views upon a domain of interest, or to attend to capture all knowledge potentially available about the respective domain.
Even after the scope of the ontology has been defined, the number of competency questions can grow very quickly modularization, prioritization.
Requirements are often contradictory prioritization.
Issues
Source: Ontology Development 101.
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Ontology EngineeringGlossary Creation: Objectives
The glossary is the reference for all further activities. It describes the terms of the ontology in a comprehensive manner.
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.
Output
List of requirements.
Input
Define terms of the ontology in natural language.
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.
Alignment of objects with business processes.
Objectives
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Ontology EngineeringGlossary 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.
(Automatic or semi-automatic) Knowledge acquisition techniques, e.g. Information Extraction, Ontology Learning.
(Semantic) wikis to collect terms.
A top-down development process starts with the definition of the most general concepts in the domain and subsequent specialization of the concepts.
A bottom-up development process starts with the definition of the most specific classes, the leaves of the hierarchy, with subsequent grouping of these classes into more general concepts.
A middle-out approach: define the more salient concepts first and then generalize and specialize them appropriately.
Describe the glossary terms in their application context, list synonyms, list domain assumptions, give examples of instances of the glossary terms.
Define hierarchical relationships between glossary terms.
Define domain relationships among glossary terms.
Align business processes with glossary terms.
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Ontology EngineeringGlossary: Examples
The glossary is the first step towards an axiomatized ontology.
Apple is a subclass of Fruit. Every apple is a fruit.
Red wines is a subclass of Wine. Every red wine is a wine.
Chianti wine is a subclass of Red wine. Every Chianti wine is a red
wine.
Hierarchy
wine, grape, winery, location, wine color, wine body,
wine flavor, sugar content, white wine, red wine,
Bordeaux wine, food, seafood, fish, meat, vegetables,
cheese…
and not
sightseeing Tuscany, atoms and molecules of alcohol, underage drinking laws…
Collect Glossary Terms Visualization
Source: Ontology Development 101.
Middlelevel
Toplevel
Bottomlevel
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Ontology EngineeringModeling: Objectives
In the modeling step the glossary terms are transferred in the target representation language.
Class descriptions.
Hierarchy.
Attributes of each class.
Associations and other type of relationships among classes.
Restrictions/constraints on classes.
Output
Glossary.
Input
Development of a machine understandable ontology.
Development of a reusable ontology.
Input for the application.
Objectives
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Ontology EngineeringModeling: 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 relationships.
Depending on the representation language different modeling primitives are available:
Cardinality, domain and range restrictions.
Hierarchies of relationships.
Inverse, functional, transitive relationships.
Equivalence.
Disjoint classes.
e.g., Protégé, Ontoprise’ OntoStudio, TopQuadrant’sTopbraid Composer, Altova, OntologyWorks, IBM, tools for thesaurus or taxonomy building.
Define and apply modeling patterns.
Reusing modeling patterns. Collections of patterns available from software engineering and modeling.
Integrate with existing application environment.
Ontology alignment and mapping. Open sources prototypes available.
Relate to upper ontology. Upper ontology. Consistency checking, ontology alignment tools.
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Ontology EngineeringModeling: 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
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.
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.
Guidelines
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Ontology EngineeringModeling: 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
EntitySpecCharacteristicvalue EntityCharacteristicvalue
EntitySpecificationEntitySpecification
EntitySpecCharacteristicEntitySpecCharacteristic
Entity Entity
0..n0..n
0..n
0..n0..n
0..n
0..n
0..n
0..1
0..1
0..1 0..n
1Entity SpecificationDescribes
Entity SpeciCharacteristicDescribes
Entity SpeciCharValueDescribes
EntitySpecCharacterizedBy
EntitySpecCharEnumeratedBy
Entity DefineBy1
E.gMobile
E.gColor
E.gChocolate, red, … E.gChocolate
EntitySpecDescribedBy
Source: TMForum, SID.
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Ontology EngineeringModeling: Examples
The business interaction pattern facilitates the representation of e.g., the communication with a client in a business context.
Business Interaction
BusinessInteraction
BusinessInteractionType
Place
BusinessInteractionRole
PartyRole ResourceInteractionRole CustomerAccountInteractionRole
BusinessInteractionLocation
1
1
0..n 0..n
0..n
0..n
0..n
0..n
BusinessInteractionTypeCategorize
BusinessInteractionInvolvesLocation BusinessInteractionRelationship
BusinessInteractionInvolves
BusinessInteractionReferences
Source: TMForum, SID.
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Ontology EngineeringTest: Objectives
The test step should ensure that the result of the modeling phase does indeed meet the requirements set in the requirements analysis phase
Refined and tested ontology.
Output
Modeled ontology.
Requirements.
Input
Tested ontology.
Running proof-of concept.
Satisfaction of the stakeholders.
Demonstration to top management that the approach works.
Early possibility to adapt approach.
Objectives
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Ontology EngineeringTest: 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 checking.
Unit tests. Often supported by ontology engineering environment.
Deploy ontology in proof-of-concept set-up.
Proof-of-concept. -
Run different test corresponding to the requirements.
Test methods known from Software Development.
Tools used in Software Development.
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Content
5. Useful MethodsOntoCom – Effort Estimation for Ontology Engineering
Modeling Guidelines
Argumentation
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Content
5. Useful Management and Support MethodsOntocom – Effort Estimation for Ontology Engineering
Modeling Guidelines
Argumentation
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Management SummaryOntocom 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.
Ontocom
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
A methodology to adjust the estimations to a particular company.
Ontocom takes the size, the domain, the development complexity, the expected quality and the experience of the staff as input factors.
Ontocom estimates ontology development costs with a 30% accuracy in 80% of the cases.
DescriptionElements
MethodologyFormula
Ontocom
Process
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OntocomProcessApplying 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.
Size estimation
Evaluation of the domain complexity
Evaluation of the
development complexity
Evaluation of the expected
qualityEvaluation of the personnel Effort estimation
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Ontocom
∏= iB
CD * ) (Size *A PM
Formula
Parametric Effort Estimation Method
Person Month
Normaliza-tion Factor
Size of the Ontology
Cost Drivers
Learning Factor
The formula uses information collected in the ontology development process and of historical information collected from previous projects to make the effort estimation.
ResultInput from project managerInput from methodology
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OntocomFormula: Example
Effort Estimation Formula
Person Month
Size of the Ontology
Cost Drivers
Quality of personnel
high
very high
average
low
very low
Development complexity
high
very high
average
low
very low
500 Entities= *6.9 PM
X
X
The parameters associated with the different cost drivers are predefined in our calculation tool.
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OntoComMethodology
Model generation
Data collection Data analysis Model Usage
Specify cost drivers Collect data Analyze data Calibrate
model
Model calibration
Evaluate model
Release model
For a high accuracy of the model we calculated the parameters aggregating the experience of over 40 ontology engineering projects. And counting.
01.0002.0003.0004.0005.0006.0007.0008.0009.000
10.00011.00012.000
0 4 8 12
16
20
24
28
32
34
average estimation
+/-30% tolerance
Effort estimations
The accuracy of the model increases if it is adapted and calibrated with data from your own business.
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OntoComProcess: Cost DriversStep 1: Size of the ontology
An ontology has
500 classes.
700 attributes.
300 relations.
no rules.
This totals in 1.5 k entities.
Examples
The size of the ontology. This includes all first class citizens of an ontology. Size is measured in kilo entities. All class definitions. All attribute definitions. All relationship definitions. All rule definitions.
Explanation
Determining the size of a prospected ontology is a challenging task in an early stage of the ontology development process.
Existing domain ontologies can help to get a rough capture.
1. Search for existing domain ontologies.
2. Compare coverage of existing domain ontologies with the required level of detail.
3. Calculate expected size of the new ontology.
Guidelines
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OntoComProcess: Cost DriversStep 2: Evaluation of the domain
An ontology for the cooking domain, having a low number of requirements and a high number of available information sources has a very low to low domain complexity.
An ontology for the chemistry domain, with a high number of requirements and a low number of available information sources has a high to very high domain complexity.
Examples
The Domain Analysis Complexity accounts for those features of the application setting which influence the complexity of the engineering outcomes. It consist of three sub categories:
The domain complexity.
The requirements complexity.
The available information sources.
Explanation
DOMAIN
Very Low: narrow scope, common-sense knowledge, low connectivity.
Very High: wide scope, expert knowledge, high connectivity.
REQUIREMENTS
Very Low few, simple requirements.
Very High: very high number of req. with a high conflicting degree, high number of usability requirements.
INFORMATION SOURCES
Very Low high number of sources in various forms.
Very High none.
Guidelines
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OntoComProcess: Cost DriversStep 3: Evaluation of the development complexity
An ontology for a search application with an thesaurus has a low development complexity.
An ontology for the chemistry domain, modeling reaction patterns has a high development complexity.
Examples
The Conceptualization Complexity accounts for the impact of a complex conceptual model on the overall costs.
The Implementation Complexity takes into consideration the additional efforts arisen from the usage of a specific implementation language.
Explanation
CONCEPTUALIZATION
Very Low: concept list.
Very High: instances, no patterns, considerable number of constraints.
IMPLEMENTATION
Low: The semantics of the conceptualization compatible to the one of the implementation language.
High: Major differences between the two.
Guidelines
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OntoComProcess: Cost DriversStep 4: Evaluation of expected quality
An ontology which is used for one application 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.
Examples
The Evaluation Complexity accounts for the additional efforts eventually invested in generating test cases and evaluating test results. This includes the effort to document the ontology.
Required reusability to capture the additional effort associated with the development of a reusable ontology,
Explanation
ONTOLOGY EVALUATION
Very Low: small number of tests, easily generated and reviewed.
Very High: extensive testing, difficult to generate and review.
REUSEABILITY
Very Low: Ontology is used for this application only.
Very High: Ontology should be used across many applications as an upper level ontology.
Guidelines
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OntoComProcess: Cost DriversStep 5: Evaluation of personnel
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.
Examples
Ontologist/Domain Expert Capability accounts for the perceived ability and efficiency of the single actors involved in the process (ontologist and domain expert) as well as their teamwork capabilities.
Ontologist/Domain Expert Experience to mea-sure the level of experience of the engineering team w.r.t. performing ontology engineering.
Explanation
ONTOLOGIST/DOMAIN EXPERT CAPABILITY
Very Low: 15%.
Very High: 95%.
ONTOLOGYIST/DOMAIN EXPERT EXPERIENCE
Very Low: 2 month (ontology) / 6 month (domain).
Very High: 3 years (ontology) / 7 years (domain).
Guidelines
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01.0002.0003.0004.0005.0006.0007.0008.0009.000
10.00011.00012.000
0 5 10 15 20 25 30 35
Entities
Changes in the development team:
The team consisted of in average 4 people.
The team structure changed quite often due to management decisions.
This required experienced modelers to train newcomers.
Aligning the process model with the ontology:
Tool support to define the data objects required for activities in a process model is limited.
The original model does not account for the integration of an ontology with a process model.
Size
The estimate of the size of the ontology is relatively good.
The project is ongoing.
OntoComCase Study: Estimated vs. Actual FiguresThe 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.
EvaluationActual Effort
no. o
f ent
ities
person month
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Content
5. Useful Management and Support MethodsOntocom – Effort Estimation for Ontology Engineering
Modeling Guidelines
Argumentation
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DefinitionModeling Guidelines
A conceptual/semantic model is a mental model which captures ideas in a domain of interest in terms of modeling primitives.
The aim of conceptual model is to express the meaning of terms and concepts used by domain experts to discuss the problem, and to find the correct relationshipsbetween different concepts.
The conceptual model attempts to clarify the meaning of various usually ambiguous terms, and ensure that problems with different interpretations of the terms and concepts cannot occur.
Once the domain of interest has been modeled, the model becomes a stable basis for subsequent development of applications in the domain.
A conceptual model can be described using various notations.
Conceptual/semantic models
A domain model is a conceptual model of a system which describes the various entities involved in the system and the relationships among them.
The domain model is created to capture the key concepts and the vocabulary of the system.
It identifies the relationships among all major entities within the system, as well as their main methods and attributes.
In this way the model provides a structural view of the system which is normally complemented by the dynamic views in use case models.
The aim of a domain model is to verify and validate the understanding of a domain of interest among various stakeholders of the project group. It is especially helpful as a communication tool and a focusing point between technical and business teams.
Domain/use case models
Influence
Ontologies are conceptual models. Modeling guidelines developed for semantic models apply to ontologies as well. Ontologies can capture domain or use case knowledge.
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PrinciplesThese are some of the most important characteristic of a semantic model.
Modeling Guidelines
A model is built according to a modeling theory. ER modeling, OO modeling,
ontologies, semantic networks, object-role modeling etc.
A model uses modeling primitives. Concepts, classes, entities,
objects, elements.
Attributes, properties, methods.
Relationships.
Axioms, constraints, restrictions, rules.
A model is represented using a particular notation. Tables and columns, XML,
UML, OWL etc.
A model describes some domain of interest in a simplified, abstract way.1
Contains structural information.2
Application-independent vs. application-dependent models.3
Shared understanding. 4
Communication.5
Reusability.6
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Modeling PrimitivesThe most important modeling primitives of an ontology are classes, attributes, associations, and rules.
Modeling Guidelines
Ontology
Relationships
Also called
Relations, associations, properties, object properties
Can have various properties.
Constraints
Define formal relationships
between modeling primitives.
Further constrain the meaning of
these elements.
Attributes
Describe the characteristics of
classes.
Classes
Represent sets of instances.
Also called
Concepts, entities
Can have equivalent classes,
inverse classes, anonymous
classes.
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ClassesClasses represent sets of instances. Typical candidates for classes are nouns in the domain of interest.
Modeling Guidelines
Example
A class represents a set of instances.
A class should be highly cohesive, precisely nameable, relevant.
A class should have a strong identity.
Definition
Interview: talk to subject matter experts.
Documentation: read what experts have written about the subject matter, read the requirements documentation, read proposals and invitations to tender.
Observation and reflection.
Classes vs. instances.
Typical candidates for classes: NOUNS. But: actors of use cases do not necessarily
correspond to classes.
Other terms as well Gerund: „My eyes glazing over…“
Verbs: an association which starts to take on attributes and associations of its own turns into an class: „Officer arrests suspect“.
Verbs: events: „Illness episode“.
Passive form: re-formulate in active form.
No pronouns.
How to find ...?
Crime Suspect
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Class HierarchyA subclass represents a concept that is a kind of the concept represented by the superclass. All instances of the subclass are instances of the superclass.
Modeling Guidelines
A subclass of a class represents a concept that is a “kind of” the concept that the superclass represents.
All instances of the subclass are instances of the superclass.
Classes represent concepts in the domain and not the words that denote these concepts.
A single person is not a subclass of all persons.
Synonyms for the same concept do not represent different classes.
All the siblings in the hierarchy must be at the same level of generality.
If a class has only one direct subclass there may be a modeling problem or the ontology is not complete.
If there are more than a dozen subclasses for a given class then additional intermediate categories may be necessary.
There must be a reason to define a subclass. Subclasses of a class usually have additional properties that the superclass does not have, or
restrictions different from those of the superclass, or
participate in different relationships than the superclasses.
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AttributesAttributes are measurable properties of classes. They are typically denoted by
Modeling Guidelines
Example
An attribute is a measurable property of an class. Scalar values: choice from a range of possibilities. An attribute is NOT a data structure. It is not
complicated to measure. Value of attributes: integer, real numbers,
enumerations, text. Attributes should have precise representative
names.
Definition
Interview: talk to subject matter experts.
Documentation: read what experts have written about the subject matter, read the requirements documentation, read proposals and invitations to tender.
Observation and reflection.
Nouns in „-ness“ Velocity-ness, job-ness, arrested-ness…
„How much, how many“ test. If you evaluate this, then it is probably an attribute.
If you enumerate classes, it is probably an entity.
Status attributes are problematic because of open-ended range or fixed, but very large possible values, or because of complex state dependencies.
How to find ...?
name:textage: integereyesight: enum{…}
Witness
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RelationshipsRelationships connected class instances. They are typically denoted by verbs and verbal phrases.
Modeling Guidelines
Example
Relationships are associations in which class instances are aware of, and characterized by, other class instances.
Properties: reflexivity, cardinality, functional, inverse-functional, many-to-many, all values from, some values of, transitivity, symmetry etc.
Definition
Interview: talk to subject matter experts.
Documentation: read what experts have written about the subject matter, read the requirements documentation, read proposals and invitations to tender.
Observation and reflection.
Verbs, verbal phrases and things that could have been verbs. But: „The butler murdered the duchess“
Naming conventions: isInvestigated, investigates, hasInvestigated, investigated.
Roles.
How to find ...?
Crime Officer* *
investigates
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ConstraintsConstraints capture in-depth knowledge of the domain of interest. They are associated to classes, attributes, and relationships.
Modeling Guidelines
Example
Constraints introduce additional restrictions of the meaning of modeling primitives.
Types: cardinality, domain and range, values.
Definition
Interview: talk to subject matter experts.
Documentation: read what experts have written about the subject matter, read the requirements documentation, read proposals and invitations to tender.
Observation and reflection.
Cardinality constraints: numbers, but also articles, plural forms, typical verbal phrases.
Domain and range constraints: typical restrictive phrases, rationales for introducing subclasses in the first place.
How to find ...?
Crime Murder1..* 1..*
commit
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Content
5. Useful MethodsOntocom – Effort Estimation for Ontology Engineering
Modeling Guidelines
Argumentation
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MotivationOntology engineering relies a common understanding of the stakeholders upon the domain of interest. Argumentation supports consensus building.
Argumentation
Reaching agreement in the ontology engineering process is difficult
Different interests of the stakeholders lead to different requirements for conceptualization of a domain.
Unsupported the discussions to reach agreement are unstructured and time consuming
In distributed scenarios discussions are documented in eMails and not traceable for outsiders
There are no guidelines for discussions
The consensus building process is not traceable
Motivation
Issue:Is the relationship “isPartOf” transitiv?
Example:Mallorca isPartOf Spain Palma di Mallorca isPartOf Mallorca
Andalusien isPartOf Spanien Gibraltar isPartOf Andalusien
Answer:In this case it depends whether the relationship should represent geographic or political isPartof relationsships.
Example
DILIGENT guides participants through their argumentation and supports faster decision making, while keeping track of the exchanged arguments.
DILIGENT Argumentation FrameworkArgumentation
DILIGENT framework
Issues are requirements on the ontology.
Ideas reflect how issues can be modeled in the ontology.
Participants exchange arguments around issues and ideas.
They can elaborate, disagree, agree, and propose alternatives to issues and ideas.
This leads to a commonly agreed ontology with traceable decisions.
The process has been proven and tested in other (engineering areas).
Description
Source: Tempich et. al., IEEE, 2008.
The argumentation ontology captures aspects of an ontology engineering discussion, starting from the requirements analysis to the modeling step. It can be used to detect inconsistencies in the argumentation process, to trace back modeling decisions, and to exchange argumentation information.
DILIGENT Argumentation OntologyArgumentation
Source: Tempich et. al., IEEE, 2008.
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6. Conclusion
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Conclusions
Ontologies are a core enabler for Enterprise Information Management.
They can facilitate communication across business units and create opportunities for new business models.
This tutorial has helped you to set-up your ontology engineering project.
Get management buy-in and define the goals of the ontology development effort.1
Agree on an ontology engineering process and stick to it.2
Know your application scenario on adapt the way you model accordingly.3
Implement early and show that it works.4
Use modeling patterns. 5
Monitor your effort and compare it with your estimations.6
Thank you.
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Contact
Dr. Christoph TempichDetecon International GmbHIndustry/Competence Practice IT
Oberkasseler Str. 253227 Bonn (Germany)Phone: +49 228 700-1942Fax: +49 228 700 – 2361Mobile: +49 (151) 12720065e-Mail: [email protected]
Dr. Elena SimperlSTI InnsbruckUniversity of InnsbruckICT TechnologieparkTechnikerstr. 21a6020 Innsbruck (Austria)Phone: +43 512 507 96884Fax: +43 512 507 9872Mobile: +43 664 812 5236e-Mail: [email protected]
Backup.
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Ontology EngineeringPointers to not covered topicsIf you are interested in languages and standards the following links may be of interest.
Languages and Standards
Natalya F. Noy and Deborah L. McGuinness: Ontology Development 101: A Guide to Creating Your First Ontology