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Ontology engineering ESTC2008


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Tutorial at the European Semantic Technology Conference ESTC 2008 on ontology engineering.

Tutorial at the European Semantic Technology Conference ESTC 2008 on ontology engineering.

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  • 1. Ontology Engineering Tutorial Dr. Elena Simperl Dr. Christoph Tempich 24.09.2008
  • 2. Ontology EngineeringPresenters Dr. Elena Simperl Dr. Christoph TempichI am working as a senior researcher in the areas of I am a management consultant in the CPsemantic systems and technologies at STI Information Technology. at Detecon International.Innsbruck, University of Innsbruck. SectorsSectors  Telecommunication, Automotive. ICT. FunctionsFunctions  Enterprise Information Management. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT R&D.  Technology markets and innovation. Education and training. ExperienceExperience  Consulting at Detecon International and Vice director STI Innsbruck, STI International Bearingpoint (KPMG). service coordinator for education.  More than 10 years Semantic Web research. 8 years of experience in Semantic Web research  Workshops and more than 40 publications. and development  Two Innovation Awards for Semantic Web Management of more than 10 national and EU applications. projects Page 1
  • 3. Ontology EngineeringManagement SummaryYou will learn how to convince your CEO to start an ontology engineering initiative andhow to implement it successfully in your company. The core objective of Information Management is to enable informed decision making. Ontologies play an increasing role in holistically organizing enterprise information. In the tutorial we will position Ontology Engineering in the broader context of Enterprise Information Management.We will introduce the five steps - setup, requirements analysis, glossary creation, modeling, test - of our methodology for developing ontologies in an enterprise context. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  For each step we will present the roles of participating actors, the methods and software available to guide and even partially automatize particular tasks, and metrics which can be used to assess the quality of the intermediary outcomes. In addition we will discuss best practices and guidelines related to critical aspects of Ontology Engineering:  Modeling specific types of knowledge.  Resolving conflicts in collaborative ontology building processes through argumentation.  The automatic generation and learning of ontologies from existing unstructured data sources.  Ontology engineering economics. Page 2
  • 4. Content1. Motivation2. Enterprise Information Management3. Ontology Engineering Methodologies4. Ontology Development ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT5. Useful Management and Support Methods6. Conclusion Page 3
  • 5. Content1. Motivation Vision Customer Challenges and Benefits Ontology Engineering Definition What Do You Expect from This Tutorial? Tutorial Overview ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 4
  • 6. MotivationVisionSeamless data integration across different data sources on the Web is a great challenge.It also promises huge business opportunities and cost savings. Web-scale data integration Description  Semantic technologies refer to techniques to help a computer program automatically process and use arbitrary data (and services) in a meaningful way.  After a decade of intensive ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT research semantic technologies seem to be the best candidate to offer the underlying technology for Web-scale data integration.  Ontologies are a core enabler of this vision.  Relevant terms: Web 2.0, Web 3.0, Semantic Web Services, Semantic Web. Page 5
  • 7. MotivationCustomer Challenges and BenefitsEnterprise Information Management aims to handle the rapidly growing amounts ofinformation relevant to an enterprise business. Challenges Benefits The amount of available and potentially relevant  A flexible information infrastructure which allows information is growing exponentially. to manage the growing amount of information. The time for decision making is decreasing  Flexible integration with business processes to continuously. account for changing business requirements. Decision making is based on multiple highly  Up-to-date information for accurate decision heterogeneous, distributed and rapidly changing making. information sources. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Fulfillment of regulatory requirements. The explosion of information is facilitated by  Timely and informed interaction with customers technical developments such as RFID , email, responding to their needs. Web, Internet.  Identification of information leakage, customer Enterprises need an intelligent information demands and cost drivers. infrastructure which delivers the right information at the right place in the right time. Data Governance is required for clear responsibilities. Page 6
  • 8. MotivationOntology Engineering Definition“the set of activities that concern the ontology development process, the ontologylife 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. MotivationWhat Do You Expect from This Tutorial?Please tell us ... Examples Content Personal Situation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Presenters Format Objectives Interaction Page 8
  • 10. MotivationTutorial OverviewIn this tutorial we focus on the ontology development process and introducemethodologies, applications scenarios and tools. Methodologies:  Distributed  Centralized Ontology Languages Development  OWL Process  RDF(S)  Requirements  SPARQL  Evaluation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Documentation Ontology Engineering Ontology Life Application Cycle Scenarios  Development  Search Tools  Maintenance  Integration  Ontology development  Storage, reasoning, alignment, Web interaction, interfaces Page 9
  • 11. Content2. Enterprise Information Management Definition Information Value Chain Market Growth Enterprise Ontologies Application Scenarios for Ontologies ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 10
  • 12. Enterprise Information ManagementDefinitionEnterprise Information Management takes an holistic view on decision relevantinformation available within an enterprise. Vision Strategy Governance Enterprise Data Social BI and Content Management Software Master Data ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Organization Performance Management and and Management Management and Search Integration Collaboration Process Enabling Infrastructure MetricsSource: Gartner 2007, EIM conference 2008, Detecon Research 2008. Page 11
  • 13. Enterprise Information Management. Information Value Chain Positioning of the Enterprise Information Management in the overall software application market. Storage/ Generation Processing Integration Analysis Presentation Archiving un-struc- Web tured Enterprise Information Management Content Analytics external Web 2.0 Sources structured structured ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Mashups Intelligent Social Networks Decision Portals, E B2B Mgmt. reports , T Integr. Fraud dashboards, Master Data Management L ERP, CRM, Detection scorecards SCM Data EAI, SOA BI / BPM internal Warehouse unstruc- Email tured Enterprise Enterprise Content Management Search Page 12For illustrative purposes and without changing the implications the value chain is displayed in a linear form.
  • 14. Enterprise Information Management.Market GrowthInformation Management software and services are the fastest growing segment in thesoftware market because they leverage information in core business applications. Market development Assessment Western European IM software market  Data integration is a core enabler for all other IM CAGR BI and initiatives. 4.53B€ performance  BI and Performance 4.10 management Management are the major 11% 3.72 Data management topics for IM. 3.35 6.2% and integration  Competing on analytics has ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 3.01 MDM 2.70 Enterprise content become a key issue, as 2.42 management and innovation leaders use 16.3% search analytics as a means to profit Social software growth. 23 % and collaboration  More important than the technical solutions for IM are 13.7% vision, strategy, policy, process and organizational 15.0% issues. 2006 2007 2008 2009 2010 2011 2012Source: Gartner, April 2008. Page 13
  • 15. Enterprise Information Management.Ontologies and Enterprise Information ManagementOntologies are at the core of Enterprise Information Management. Master Data Data Integration Management Structured Information Social Networks ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Enterprise Ontology Unstructured Business Analytics Information Data Governance Page 14
  • 16. Enterprise Information Management.Classification of OntologiesOntologies can be classified according their formality. You might be familiar with someof these categories. Thessauri General Formal Frames Logical “narrower term” Catalog/ID is-a (properties) constraints relation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Terms/ Informal Formal Value Disjointness, glossary is-a instance Restrs. Inverse, part-Of ...Source:Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory.Stanford University. KSL-01-02. 2001. Page 15
  • 17. Enterprise Information Management.Types of OntologiesOntologies can be classified according to their degree of reusability. R Application Domain Application Domain E Ontology Task Ontology U S Domain Ontology Domain Task Ontology A Core Ontology Task Ontology B ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT I Top-Level Ontology L I General/Common Ontology T Y Representation Ontology Page 16
  • 18. Enterprise Information Management.Application Scenarios for OntologiesOntologies are a means to enable interoperability between machines. They also facilitatecommunication between people providing a shared representation of a domain. Neutral Authoring Ontology-based Search  Bases for application  Ontologies provide the development as core data structure for the navigation of model for all applications. the results, support browsing and classification.  Typical use case in AI.  Ontologies allow for term disambiguation and query rewriting. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Ontologies  Global view on information.  Specification of software systems and automation of  Organization and code generation. management of information sources and their interrelation.  MDA.  Consistency checking.  SOA.  Currently most relevant use case for enterprises. Common Access to Information Ontology-based SpecificationSource: Jasper & Uschold, 1999. Page 17
  • 19. Enterprise Information Management.Application Scenarios for Ontologies: Common Access to InformationBuilding a semantic application is feasible. However it still requires deep technologyinsight and best practices for system integration are still under development. Ontology Mapping of Feasibility Requirements development data sources Set-up storage ApplicationStakeholders* Consultants (few  System  Technology  Technology  Technicians  System consultants integrators (only consultants (Few consultants (Scalable stores integrators (user available small players). consultants, (Technology available, good interfaces understanding engineering requires deep vendor support). available,  Weakest part in the benefit). environment technical skills).  Integration with customization the value chain. available). all kinds of data required, Decision makers  Technology ready bases possible. expertise not (technicians, less  Staff (requires for structured da- available). ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT C-level knowledge ta sources, not awareness) . transfer). ready for unstruc- tured sources.Key Deliverables Feasibility study.  Detailed  Ontology.  Mapping of data  Integration of  User interface. Identification of description of sources to triplestores with  Sample queries.  Process to data stores to be application. ontology. data sources by means of support integrated.  Translation.  Evaluation  Enterprise application. mappings. Business case- criteria.  Logical model. ontology based  SOA SOA messages.  Scalable storage  Detailed  Documentation of infrastructure. Rough solution. application description of ontology. data sources.  Maintainability. architecture.  Maintainability of  Application ontology. scenario *Who should do it (current problems) Page 18
  • 20. Content3. Ontology Engineering Methodologies Historical Background Methodologies Related to Knowledge Management Systems Methodologies Related to Software Engineering Distributed Ontology Engineering New Approaches ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Condensed Version Page 19
  • 21. Ontology Engineering MethodologiesOntology Engineering Definition“a comprehensive, integrated series of techniques or methods creating a generalsystems theory of how a class of thought-intensive work ought be performed”* Typical Elements of a Methodology Description Application scenario  The application scenario of a methodology describes the general settings to which the methodology is applicable. Process Process ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  The process describes the activities and tasks, including their sequence, input and Application output, to be performed by the scenario stakeholders. Tools Roles Roles  Describe the responsibilities and tasks of different stakeholders in the process.*Source: IEEE, 1990. Page 20
  • 22. Ontology Engineering MethodologiesHistorical BackgroundThe development of ontology engineering methodologies has a long history and wasstrongly influenced by specific project experiences of the authors. CommonKADS Enterprise Ontology [Schreiber et al., 1999] [Uschold & King, 1995] ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT IDEF5 [Benjamin et al. 1994] Holsapple&Joshi [Holsapple & Joshi, 2002] CO4 [Euzenat, 1995] Page 21
  • 23. Ontology Engineering MethodologiesMethodologies Related to Knowledge Management SystemsThe On-To-Knowledge methodology takes a pragmatic approach to ontology engineeringand contains many useful tips to support non-experts to build an ontology. Go / Sufficient Meets Roll-out? Changes? Description No Go? requirements requirements ? ? Application scenario Common ORSD + Target Evaluated Evolved KADS Semi-formal ontology ontology ontology  Suited for developing Human Worksheets ontology description ontologies for knowledge Issues management applications. Refine- Evalu- Application Knowledge Feasibility study Kickoff ment ation & Management Process ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Evolution Application  Sequential/iterative process. SoftwareIdentify .. 5. Capture 7. Refine semi- 10. Technology- 13. Apply Engineering Roles1. Problems & requirements formal ontology focussed ontology opportunities specification in description evaluation 14. Manage ORSD  Ontology engineers design the2. Focus of KM 8. Formalize into 11. User- evolution and application 6. Create semi- target ontology focussed maintenance ontology and interact with3. (OTK-) Tools formal ontology 9. Create evaluation domain experts to develop it. description Prototype 12. Ontology-4. People focussed evaluation Ontology DevelopmentSource: Sure, 2003. Page 22
  • 24. Ontology Engineering MethodologiesMethodologies Related to Software EngineeringMETHONTOLOGY contains the most comprehensive description of ontology engineeringactivities. It is targeted at ontology engineers. Ontology Management Scheduling, controlling, quality assurance Description Application scenario Feasibility study Problems, opportunities, potential solutions, economic feasibility  Generic methodology for ontology development in centralized settings. Knowledge acquisition Knowledge acquisition Domain analysis motivating scenarios, competency questions, existing solutions Ontology reuse Ontology reuse Documentation Process Evaluation Conceptualization  Serial process comparable to ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT conceptualization of the model, integration and extension of the waterfall model in software existing solutions engineering. Implementation Roles implementation of the formal model in a representation language  The ontology is developed by ontology engineers. Maintenance adaptation of the ontology according to new requirements  Users are not directly involved in the engineering process. Use ontology based search, integration, negotiationSource: METHONTOLOGY, Gómez-Pérez, A. ,1996. Page 23
  • 25. Ontology Engineering MethodologiesDistributed Ontology EngineeringDILIGENT is a methodology for distributed ontology engineering. It focuses onconsensus building aspects through argumentation. Description 2. Local Adaptation Application scenario 1. Central O1 3. Central  Generic methodology for Build Analysis ontology development in 5. Local decentralized settings. Update Process OI O-User 1  Rapid prototyping process Ontology … ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT with short update cycles. User Domain Ontology Board Expert Engineer Roles On  Users actively participate in O-User n the ontology engineering process. Knowledge  Modeling decisions are made Engineer 4. Central by a board including ontology Revision engineers.Source: DILIGENT: Tempich, 2006. Page 24
  • 26. Ontology Engineering MethodologiesNew ApproachesRecent methodologies concentrate on decentralization. They apply Web 2.0 paradigms inorder to facilitate the development of community-driven ontologies. Wikis Games Tagging  Employing Wikis in  Usage of games with  Tagging is a very ontology engineering a purpose to motivate successful approach enables easy humans to undertake to organize all sorts of participation of the complex activities in content on the Web. community and lowers the ontology life cycle.  Tags often describe barriers of entry for non-  During such a game, the meaning of the experts. players describe tagged content in one images, text or term. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  So far less suitable for developing complex, videos. Players  Approaches to derive highly axiomatized receive a higher score formal ontologies from ontologies. if they describe the tag clouds are content in the same emerging. way. Ontology engineering increasingly becomes an community activity.Source: Siorpaes 2008, Braun 2007. Page 25
  • 27. Ontology Engineering MethodologiesCondensed VersionIn our project experience we found out that a number of process steps and activitiesdistinguish ontology development from related engineering efforts. They are crucial forthe success of an ontology development project. Requirements analysis motivating scenarios, use cases, existing solutions, Knowledge acquisition effort estimation, competency questions, application requirements Test (Evaluation) Documentation Glossary creation (Conceptualization) ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT conceptualization of the model, integration and extension of existing solutions Modeling (Implementation) implementation of the formal model in a representation language Page 26
  • 28. Content4. Ontology Engineering Overview Set-up Requirements Analysis Glossary Creation Modeling ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Testing Page 27
  • 29. Ontology EngineeringOverviewWe present an ontology engineering process consisting of 5 steps. We describe thoseaspects of the engineering process which are essential for its successfulimplementation. Condensed Ontology Engineering Process Elements to Be Discussed Requirements analysis motivating scenarios, use cases, existing solutions, Knowledge acquisition effort estimation, competency questions, application requirements Test (Evaluation) Documentation Glossary (Conceptualization) conceptualization of the model, integration and extension of existing solutions Modeling (Implementation) implementation of the formal model in a representation language ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Objectives Req. Glossary analysis creation Process step Set-up Ontology Methods, Examples activities Test and tools Modeling Page 28
  • 30. Ontology EngineeringSet-up: ObjectivesIn the set-up phase the project manager organizes the ontology development project andgets the buy-in of all stakeholders in order to enable a smooth project implementation. Objectives Input Buy-in of all stakeholders (management, project  Management contract to design an ontology. managers, business units, developers) to the  Business objectives and alignment with proposed ontology engineering process. business strategy. The scope of the ontology in terms of domains is  Business goals and business drivers. clear. The application scenario for the ontology is defined (integration, search, communication, ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT etc). The number and types of applications are Output defined.  Defined ontology engineering process. The proposed tool chain works smoothly.  Defined high-level application scenario  List of stakeholders.  List of relevant domains to be modeled.  Operational tool chain.  Training material. Page 29
  • 31. Ontology EngineeringSet-up: Methods, activities and toolsThe set-up step can be completed within one month. Activities Methods ToolsDefine 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 toolsmanagement estimation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPTSelect 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 Semantic Arts Inc. (upper ontology), OASIS (standardizationontologies body) Internal definitions, thesauri, glossaries, hierarchies, domain models.Set-up tool chain Proof of Specify requirements for tracking tool, glossary documentation tool, concept ontology engineering environment, ontology learning tool (if applicable), data integration tool, reasoner, SOA environment, triplestore, enterprise applications (if applicable), representation language. Page 30
  • 32. Ontology 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. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPTSemantic search. Semi-formal ontology. Usage of natural language labels and naming conventions. Well-balanced at schema and instance level. Rich conceptualization. Syntactical and semantic correctness.The ontology should not contain all the possible information about the domain of interest. Page 31
  • 33. Ontology EngineeringSet-up: ExamplesExamples of existing reusable ontologies are the TMForum SID domain model and theGIST upper ontology. SID domain model GIST upper ontology ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 32
  • 34. Ontology EngineeringRequirements Analysis: ObjectivesIn the requirements analysis step the project team collects the expectations from thestakeholders towards the ontology. Objectives Input Guidelines for the modeling phase.  Scope and application scenario of the ontology. Criteria for the evaluation of the engineering  Information sources. effort.  Domain of the ontology. Agreement on ontology use cases for the ontology between stakeholders. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT The development of the ontology is pursued in monthly cycles. Output Although requirements can be collected at all times, they should be prioritized.  Competency questions and use case descriptions forming the list of requirements. We select an excerpt of the total list of requirements such that they can be implemented and tested within one month. Page 33
  • 35. Ontology EngineeringRequirements Analysis: Methods, activities and toolsCollecting competency questions is a proven method to describe the requirements foran ontology. Activities Methods ToolsCollect Collaboration, brainstorming. (Semantic)wiki torequirements store and describe Competency questions requirements.  A set of queries which place demands on the underlying ontology.  Ontology must be able to represent the questions using its terminology and the answers based on the axioms.  Ideally, in a staged manner, where consequent questions require the input from the preceding ones. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  A rationale for each competency question should be given.Discuss and Argumentation, workshops DILIGENTselect relevant argumentationrequirements framework.Align with Workshopsbusiness  Collect requirements from the business process owners and alignprocess them with the information needs in the respective process. Page 34
  • 36. Ontology EngineeringRequirements Analysis: ExamplesWe present examples of requirements produced in this step. Competency Questions Other Requirements Issues  Which wine characteristics  Concepts in the ontology  An ontology reflects an should I consider when should be bi-lingual. abstracted view of a domain of choosing a wine? interest. You should not  The ontology should not have model all possible views upon  Is Bordeaux a red or white more than 10 inheritance a domain of interest, or to wine? levels. attend to capture all  Does Cabernet Sauvignon go  The ontology should be knowledge potentially well with seafood? extended and maintained by available about the respective ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT non-experts. domain.  What is the best choice of wine for grilled meat?  The ontology should be used  Even after the scope of the to build an online restaurant ontology has been defined,  Which characteristics of a guide. the number of competency wine affect its appropriateness questions can grow very for a dish?  The ontology should be usable quickly  modularization, on an available collection of  Does a flavor or body of a prioritization. restaurant descriptions written specific wine change with in German.  Requirements are often vintage year? contradictory  prioritization.Source: Ontology Development 101. Page 35
  • 37. Ontology EngineeringGlossary Creation: ObjectivesThe glossary is the reference for all further activities. It describes the terms of theontology in a comprehensive manner. Objectives Input Define terms of the ontology in natural language.  List of requirements. Build up the body of knowledge of the terms used in an organization. Facilitate communication within the organization. Buy-in from all stakeholders in terms of selected objects, descriptions and relationships. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Alignment of objects with business processes. Output  Important terms of the domain.  Descriptions of the terms with examples.  Usage scenarios of the terms in the process.  High-level relationships between terms.  Alignment of glossary terms an business processes. Page 36
  • 38. Ontology EngineeringGlossary Creation: Methods, activities and toolsWiki technology is very suitable to support the creation and documentation of theglossary, because it enables easy collaboration and access. Activities Methods ToolsCollect glossary terms. Workshops, collaboration. (Semantic) wikis to collect terms.Describe the glossary terms in (Automatic or semi- A top-down development processtheir application context, list automatic) Knowledge starts with the definition of the mostsynonyms, list domain acquisition techniques, e.g. general concepts in the domain andassumptions, give examples of Information Extraction, subsequent specialization of theinstances of the glossary terms. Ontology Learning. concepts. A bottom-up development process ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPTDefine hierarchical relationships starts with the definition of the mostbetween glossary terms. specific classes, the leaves of theDefine domain relationships hierarchy, with subsequent grouping ofamong glossary terms. these classes into more general concepts. A middle-out approach: define the more salient concepts first and thenAlign business processes with generalize and specialize themglossary terms. appropriately. Page 37
  • 39. Ontology EngineeringGlossary: ExamplesThe glossary is the first step towards an axiomatized ontology. Collect Glossary Terms Hierarchy Visualization  wine, grape, winery, location,  Apple is a subclass of Fruit. Top wine color, wine body,  Every apple is a fruit. level  wine flavor, sugar content,  Red wines is a subclass of white wine, red wine, Wine.  Bordeaux wine, food, seafood,  Every red wine is a wine. fish, meat, vegetables, Middle  Chianti wine is a subclass of level ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  cheese… Red wine.  Every Chianti wine is a red wine. and not  sightseeing Tuscany, atoms and molecules of alcohol, underage drinking laws… Bottom levelSource: Ontology Development 101. Page 38
  • 40. Ontology EngineeringModeling: ObjectivesIn the modeling step the glossary terms are transferred in the target representationlanguage. Objectives Input Development of a machine understandable  Glossary. ontology. Development of a reusable ontology. Input for the application. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Output  Class descriptions.  Hierarchy.  Attributes of each class.  Associations and other type of relationships among classes.  Restrictions/constraints on classes. Page 39
  • 41. Ontology EngineeringModeling: Methods, activities and toolsThe complexity of the modeling step depends on the representation language and onthe complexity of the requirements. Activities Methods ToolsDefine classes, their attributes and Depending on the representation e.g., Protégé, Ontoprise’relationships. language different modeling OntoStudio, TopQuadrant’s primitives are available: Topbraid Composer, Altova, OntologyWorks, IBM, tools for  Cardinality, domain and thesaurus or taxonomy building. range restrictions.  Hierarchies of relationships. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Inverse, functional, transitive relationships.  Equivalence.  Disjoint classes.Define and apply modeling Reusing modeling patterns. Collections of patterns availablepatterns. from software engineering and modeling.Integrate with existing application Ontology alignment and mapping. Open sources prototypesenvironment. available.Relate to upper ontology. Upper ontology. Consistency checking, ontology Page 40 alignment tools.
  • 42. Ontology EngineeringModeling: ExamplesOntology development is supported by a variety of tools. Besides OWL and RDFS, UMLis gaining increasing attention as an ontology modeling language. Ontology in UML Guidelines  There is no unique way to model a domain correctly — there are always viable alternatives. The best solution always depends on the application that you have in mind and the extensions that you anticipate. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Ontology development is necessarily an iterative process.  Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain. Page 41
  • 43. Ontology EngineeringModeling: ExamplesThe entity specification pattern allows to add characteristics to an entity withoutchanging 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 EntitySpecCharacterizedByEntitySpecDescribedBy 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 SpeciCharValueDescribesSource: TMForum, SID. Page 42
  • 44. Ontology EngineeringModeling: ExamplesThe business interaction pattern facilitates the representation of e.g., the communicationwith 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 InteractionRoleSource: TMForum, SID. Page 43
  • 45. Ontology EngineeringTest: ObjectivesThe test step should ensure that the result of the modeling phase does indeed meet therequirements set in the requirements analysis phase Objectives Input Tested ontology.  Modeled ontology. Running proof-of concept.  Requirements. Satisfaction of the stakeholders. Demonstration to top management that the approach works. Early possibility to adapt approach. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Output  Refined and tested ontology. Page 44
  • 46. Ontology EngineeringTest: Methods, activities and toolsIn the test phase the stakeholders get a direct feedback if their effort has beensuccessful. Activities Methods ToolsTest queries and consistency Unit tests. Often supported by ontologychecking. 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 Softwarethe requirements. Software Development. Development. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 45
  • 47. Content5. Useful Methods OntoCom – Effort Estimation for Ontology Engineering Modeling Guidelines Argumentation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 46
  • 48. Content5. Useful Management and Support Methods Ontocom – Effort Estimation for Ontology Engineering Modeling Guidelines Argumentation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 47
  • 49. OntocomManagement SummaryOntocom is a framework to help you estimate the effort related to the building of anontology. It make accurate predictions and can be improved with data from your team. Elements Description  Ontocom is a framework to estimate the effort related to ontology development.  Ontocom comes with  A process for effort estimation.  A formula and a tool calculating the estimations. and ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Process  A methodology to adjust the estimations to a particular company.  Ontocom takes the size, the domain, the Ontocom development complexity, the expected quality and the experience of the staff as input factors. Formula Methodology  Ontocom estimates ontology development costs with a 30% accuracy in 80% of the cases. Page 48
  • 50. OntocomProcessApplying Ontocom is easy and follows a five step process. The project manager definesthe different parameters based on the process guidelines which are part of the framework. Evaluation of Evaluation of Evaluation of Size the Evaluation of ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT the domain the expected Effort estimation estimation development the personnel complexity quality complexity Page 49
  • 51. OntocomFormulaThe formula uses information collected in the ontology development process and ofhistorical information collected from previous projects to make the effort estimation. Parametric Effort Estimation Method PM = A * (Size ) * ∏ CD i B ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Person Normaliza- Size of the Cost Month tion Factor Ontology Drivers Learning Factor Result Input from project manager Input from methodology Page 50
  • 52. OntocomFormula: ExampleThe parameters associated with the different cost drivers are predefined in ourcalculation tool. Effort Estimation Formula Person Size of the Cost Month Ontology Drivers Quality of personnel Development complexity ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT very high very high 6.9 PM = 500 Entities * high X high X average average low low very low very low Page 51
  • 53. OntoComMethodologyFor a high accuracy of the model we calculated the parameters aggregating theexperience 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 4The accuracy of the model increases if it is adapted and calibrated with data from your own business. Page 52
  • 54. OntoComProcess: Cost DriversStep 1: Size of the ontology Explanation GuidelinesThe size of the ontology. This includes all first class  Determining the size of a prospected ontology iscitizens of an ontology. Size is measured in kilo a challenging task in an early stage of theentities. ontology development process. All class definitions.  Existing domain ontologies can help to get a All attribute definitions. rough capture. All relationship definitions. 1. Search for existing domain ontologies. All rule definitions. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 2. Compare coverage of existing domain ontologies with the required level of detail. Examples 3. Calculate expected size of the new ontology.An ontology has 500 classes. 700 attributes. 300 relations. no rules.This totals in 1.5 k entities. Page 53
  • 55. OntoComProcess: Cost DriversStep 2: Evaluation of the domain Explanation GuidelinesThe Domain Analysis Complexity accounts for DOMAINthose features of the application setting which  Very Low: narrow scope, common-senseinfluence the complexity of the engineering knowledge, low connectivity.outcomes. It consist of three sub categories: The domain complexity.  Very High: wide scope, expert knowledge, high connectivity. The requirements complexity. REQUIREMENTS The available information sources. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Very Low few, simple requirements. Examples  Very High: very high number of req. with a high conflicting degree, high number of usability An ontology for the cooking domain, having a requirements. low number of requirements and a high number of available information sources has a very low INFORMATION SOURCES to low domain complexity.  Very Low high number of sources in various An ontology for the chemistry domain, with a forms. high number of requirements and a low number  Very High none. of available information sources has a high to very high domain complexity. Page 54
  • 56. OntoComProcess: Cost DriversStep 3: Evaluation of the development complexity Explanation Guidelines The Conceptualization Complexity accounts CONCEPTUALIZATION for the impact of a complex conceptual model  Very Low: concept list. on the overall costs.  Very High: instances, no patterns, considerable The Implementation Complexity takes into number of constraints. consideration the additional efforts arisen from the usage of a specific implementation language. IMPLEMENTATION ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Low: The semantics of the conceptualization Examples compatible to the one of the implementation language. An ontology for a search application with an thesaurus has a low development complexity.  High: Major differences between the two. An ontology for the chemistry domain, modeling reaction patterns has a high development complexity. Page 55
  • 57. OntoComProcess: Cost DriversStep 4: Evaluation of expected quality Explanation Guidelines The Evaluation Complexity accounts for the ONTOLOGY EVALUATION additional efforts eventually invested in  Very Low: small number of tests, easily generating test cases and evaluating test generated and reviewed. results. This includes the effort to document the ontology.  Very High: extensive testing, difficult to generate and review. Required reusability to capture the additional effort associated with the development of a REUSEABILITY ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT reusable ontology,  Very Low: Ontology is used for this application only. Examples  Very High: Ontology should be used across An ontology which is used for one application many applications as an upper level ontology. only without extensive testing has a low factor. An integration ontology which should be used across an entire organization or for many web users with high documentation requirements has a high or very high factor. Page 56
  • 58. OntoComProcess: Cost DriversStep 5: Evaluation of personnel Explanation Guidelines Ontologist/Domain Expert Capability accounts ONTOLOGIST/DOMAIN EXPERT CAPABILITY for the perceived ability and efficiency of the  Very Low: 15%. single actors involved in the process (ontologist and domain expert) as well as their teamwork  Very High: 95%. capabilities. ONTOLOGYIST/DOMAIN EXPERT EXPERIENCE Ontologist/Domain Expert Experience to mea-  Very Low: 2 month (ontology) / 6 month sure the level of experience of the engineering (domain). ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT team w.r.t. performing ontology engineering.  Very High: 3 years (ontology) / 7 years Examples (domain). The new project member who has never worked with ontologies nor has any experience with the domain has a very low expert experience. The project manager who has been working with ontologies for several years and is experienced in a certain field has a very high expert experience. Page 57
  • 59. OntoComCase Study: Estimated vs. Actual FiguresThe actual effort was higher than expected. This is mainly due to frequent changes in themodeling 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. Entitiesno. of entities 8.000  This required experienced modelers to train 7.000 newcomers. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 6.000 Aligning the process model with the ontology: 5.000  Tool support to define the data objects required 4.000 for activities in a process model is limited. 3.000  The original model does not account for the 2.000 integration of an ontology with a process model. 1.000 Size 0  The estimate of the size of the ontology is 0 5 10 15 20 25 30 35 relatively good. person month  The project is ongoing. Page 58
  • 60. Content5. Useful Management and Support Methods Ontocom – Effort Estimation for Ontology Engineering Modeling Guidelines Argumentation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 59
  • 61. Modeling GuidelinesDefinitionOntologies are conceptual models. Modeling guidelines developed for semantic modelsapply to ontologies as well. Ontologies can capture domain or use case knowledge. Conceptual/semantic models Domain/use case models  A conceptual/semantic model is a mental  A domain model is a conceptual model of model which captures ideas in a domain of a system which describes the various interest in terms of modeling primitives. entities involved in the system and the relationships among them.  The aim of conceptual model is to express the meaning of terms and concepts used  The domain model is created to capture by domain experts to discuss the problem, the key concepts and the vocabulary of and to find the correct relationships the system. between different concepts.  It identifies the relationships among all ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  The conceptual model attempts to clarify major entities within the system, as well the meaning of various usually as their main methods and attributes. ambiguous terms, and ensure that Influence  In this way the model provides a problems with different interpretations of structural view of the system which is the terms and concepts cannot occur. normally complemented by the dynamic  Once the domain of interest has been views in use case models. modeled, the model becomes a stable  The aim of a domain model is to verify and basis for subsequent development of validate the understanding of a domain of applications in the domain. interest among various stakeholders of the  A conceptual model can be described project group. It is especially helpful as a using various notations. communication tool and a focusing point between technical and business teams. Page 60
  • 62. Modeling GuidelinesPrinciplesThese are some of the most important characteristic of a semantic model.  A model is built according1 A model describes some domain of interest in a simplified, abstract way. to a modeling theory.  ER modeling, OO modeling, ontologies, semantic networks, object-role2 Contains structural information. modeling etc.  A model uses modeling primitives.3 Application-independent vs. application-dependent models.  Concepts, classes, entities, ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT objects, elements.  Attributes, properties,4 Shared understanding. methods.  Relationships.  Axioms, constraints,5 Communication. restrictions, rules.  A model is represented using a particular notation.6 Reusability.  Tables and columns, XML, UML, OWL etc. Page 61
  • 63. Modeling GuidelinesModeling PrimitivesThe most important modeling primitives of an ontology are classes, attributes,associations, and rules. Classes RelationshipsRepresent sets of instances. Also calledAlso called  Relations, associations, properties, object properties Concepts, entities Can have various properties.Can have equivalent classes,inverse classes, anonymous ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPTclasses.Describe the characteristics of Ontology Define formal relationshipsclasses. between modeling primitives. Further constrain the meaning of these elements. Attributes Constraints Page 62
  • 64. Modeling GuidelinesClassesClasses represent sets of instances. Typical candidates for classes are nouns in thedomain of interest. Definition How to find ...? A class represents a set of instances.  Interview: talk to subject matter experts. A class should be highly cohesive, precisely  Documentation: read what experts have written nameable, relevant. about the subject matter, read the requirements documentation, read proposals and invitations to A class should have a strong identity. tender.  Observation and reflection.  Classes vs. instances. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Typical candidates for classes: NOUNS. Example  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 Crime Suspect 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. Page 63
  • 65. Modeling GuidelinesClass HierarchyA subclass represents a concept that is a kind of the concept represented by thesuperclass. All instances of the subclass are instances of the superclass. 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. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT 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. Page 64
  • 66. Modeling GuidelinesAttributesAttributes are measurable properties of classes. They are typically denoted by Definition How to find ...? An attribute is a measurable property of an  Interview: talk to subject matter experts. class.  Scalar values: choice from a range of possibilities.  Documentation: read what experts have written  An attribute is NOT a data structure. It is not about the subject matter, read the requirements complicated to measure. documentation, read proposals and invitations to Value of attributes: integer, real numbers, tender. enumerations, text.  Observation and reflection. Attributes should have precise representative ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT names.  Nouns in „-ness“  Velocity-ness, job-ness, arrested-ness… Example  „How much, how many“ test. Witness  If you evaluate this, then it is probably an attribute.  If you enumerate classes, it is probably an entity. name:text  Status attributes are problematic because of age: integer open-ended range or fixed, but very large eyesight: possible values, or because of complex state enum{…} dependencies. Page 65
  • 67. Modeling GuidelinesRelationshipsRelationships connected class instances. They are typically denoted by verbs and verbalphrases. Definition How to find ...? Relationships are associations in which class  Interview: talk to subject matter experts. instances are aware of, and characterized by,  Documentation: read what experts have written other class instances. about the subject matter, read the requirements Properties: reflexivity, cardinality, functional, documentation, read proposals and invitations to inverse-functional, many-to-many, all values tender. from, some values of, transitivity, symmetry etc.  Observation and reflection. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Verbs, verbal phrases and things that could have Example been verbs.  But: „The butler murdered the duchess“  Naming conventions: isInvestigated, * * investigates, hasInvestigated, investigated. Crime Officer  Roles. investigates Page 66
  • 68. Modeling GuidelinesConstraintsConstraints capture in-depth knowledge of the domain of interest. They are associated toclasses, attributes, and relationships. How to find ...? Definition Constraints introduce additional restrictions of  Interview: talk to subject matter experts. the meaning of modeling primitives. Types: cardinality, domain and range, values.  Documentation: read what experts have written about the subject matter, read the requirements documentation, read proposals and invitations to tender.  Observation and reflection. ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT  Cardinality constraints: numbers, but also Example articles, plural forms, typical verbal phrases.  Domain and range constraints: typical restrictive phrases, rationales for introducing subclasses in the first place. 1..* 1..* Crime Murder commit Page 67
  • 69. Content5. Useful Methods Ontocom – Effort Estimation for Ontology Engineering Modeling Guidelines Argumentation ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 68
  • 70. ArgumentationMotivationOntology engineering relies a common understanding of the stakeholders upon thedomain of interest. Argumentation supports consensus building. Motivation Example Reaching agreement in the ontology engineering  Issue: process is difficult Is the relationship “isPartOf” transitiv? Different interests of the stakeholders lead to  Example: different requirements for conceptualization of a Mallorca isPartOf Spain domain. Palma di Mallorca isPartOf Mallorca Unsupported the discussions to reach agreement are unstructured and time consuming  Andalusien isPartOf Spanien ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Gibraltar isPartOf Andalusien In distributed scenarios discussions are documented in eMails and not traceable for  Answer: outsiders In this case it depends whether the relationship should represent geographic or political isPartof There are no guidelines for discussions relationsships. The consensus building process is not traceable Page 69
  • 71. ArgumentationDILIGENT Argumentation FrameworkDILIGENT guides participants through their argumentation and supports faster decisionmaking, while keeping track of the exchanged arguments. DILIGENT framework Description  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).Source: Tempich et. al., IEEE, 2008.
  • 72. ArgumentationDILIGENT Argumentation OntologyThe argumentation ontology captures aspects of an ontology engineering discussion, starting fromthe requirements analysis to the modeling step. It can be used to detect inconsistencies in theargumentation process, to trace back modeling decisions, and to exchange argumentationinformation.Source: Tempich et. al., IEEE, 2008.
  • 73. 6. ContentPage 72 Conclusion ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT
  • 74. Conclusions  Ontologies are a core Get management buy-in and define the goals of the1 enabler for Enterprise ontology development effort. Information Management.2 Agree on an ontology engineering process and stick to it.  They can facilitate communication Know your application scenario on adapt the way you across business units3 model accordingly. and create ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT opportunities for new business models.4 Implement early and show that it works.  This tutorial has helped you to set-up5 Use modeling patterns. your ontology engineering project.6 Monitor your effort and compare it with your estimations. Page 73
  • 75. Thank you.
  • 76. Contact Dr. Elena Simperl STI Innsbruck University of Innsbruck ICT Technologiepark Technikerstr. 21a 6020 Innsbruck (Austria) Phone: +43 512 507 96884 Fax: +43 512 507 9872 Mobile: +43 664 812 5236 e-Mail: ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Dr. Christoph Tempich Detecon International GmbH Industry/Competence Practice IT Oberkasseler Str. 2 53227 Bonn (Germany) Phone: +49 228 700-1942 Fax: +49 228 700 – 2361 Mobile: +49 (151) 12720065 e-Mail: Page 75
  • 77. Backup.
  • 78. 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 ESTC2008_ONTOLOGYENGINEERINGTUTORIAL_CT_V04_080916.PPT Page 77