Eswcsummerschool2010 ontologies final
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Transcript of Eswcsummerschool2010 ontologies final
KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)2
A LITTLE HISTORY
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ontology vocabularymicroformat conceptual graph
topic map thesaurusschema
classification object model
semantic network
glossary taxonomy
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Focus on knowledgerepresentation andreasoning
Academic topic
Research prototypesof ontology-based *
Standardization
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Focus on dataintegration, community-driveninitiative on datapublishing
Community ofdevelopers anddata and contentproviders
Leveragingmaturing semantictechnologies, andother trends (open access)
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It was never a simple matter
What exists?
What is?
What am I?
Ontologies and the Semantic Web / Ontologies - A Brief History - 6
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And we’re back to where it all started
Greek etymology (ontos = of being; logia = science, study, theory)
Parmenides of Elea, ancient Greek philosopher, early 5th century BC
Parmenides made the ontological argument against nothingness, essentially denying the possible existence of a void.
“For never shall this prevail, that things that are not are”
Ontologies and the Semantic Web / Ontologies - A Brief History - 7
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Closer to our time
Jacob Lorhard, German philosopher (1561 - 1609)
First occurrence of the word Ontology (lat. Ontologia) and the first published ontology in 1607
Translation from: Historical and conceptual foundations of diagrammatical ontology. P. Øhrstøm, S. Uckelman; H. Schärfe
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Ontologies (or whatever you call them) in Computer Science
An ontology defines • Concepts• Relationships• Any other distinctions that are relevant to
capture and model knowledge from a domain of interest
Ontologies are used toShare a common understanding about a domain among people or machinesEnable reuse of domain knowledge
This is achieved by Agree on meaning and representation of domain knowledgeMake domain assumptions explicitSeparate domain knowledge from the operational knowledge
Application areasNatural language processing
Artificial intelligence
Digital libraries
Software engineering
Database design
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Agree on meaning and representation(define-class Travel (?travel)
"A journey from place to place":axiom-def ( .... )
:iff-def (and (arrivalDate ?travel Date)
(departureDate ?travel Date)):def
(and (singleFare ?travel Number)(companyName ?travel String)))
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Make domain assumptions explicit
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Separate domain and operational knowledge
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ONTOLOGIES AND SEMANTICTECHNOLOGIES
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Semantic technologies revisited
Data is self-describing
Data items are inter-connected
Applications can derive new knowledge from existing data
AdvantagesScalable interoperabilityEnhanced information managementFlexible application engineering (if you have proficient developers)
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Semantic technologies at BestBuy
Goal: “to provide more visibility to products, services and locations to humans and machines”
Search engines identify the data more easily and put it into context (30% increase in search traffic)
Improved consumer experience
Due to “Increasing product and service visibility through front-end semantic web” by Jan Myers, SemTech 2010
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Semantic technologies at BestBuy
Data is marked-upusing RDFa andrefers to conceptsfrom a pre-definedeCommerce ontology.
Markup is entered byBestBuy staff via online forms thatproduce RDFa.
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Semantic technologies in life sciencesMedical terminologies reflect a common agreement on the types of things people talk about in medical science, and their properties and relationships.
Ontologies provide a specification of these conceptual models using formal languages.
Advantages:As a standardized vocabulary: facilitate communicationInteroperability: standardization of data exchange formats, automatized integration, interlinkingEnhanced information management: biological objects annotated using the ontology; improved navigation and filtering.
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Features of an ontology
Models knowledge about a specific domain
Reflects the shared understanding of a group of stakeholdersabout that domain
DefinesA common vocabularyThe meaning of termsHow terms are interrelated
Consists ofConceptualization and implementation
ContainsOntological primitives: classes, instances, properties, axioms/constraints…
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Classifications of ontologies
Lassila O, McGuiness D. The Role of Frame-Based Representation on the Semantic Web. Technical Report. Knowledge Systems Laboratory. Stanford University. KSL-01-02. 2001.
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Classifications of ontologies (2)
Issue of the conceptualizationUpper-level/Top-levelCoreDomainTaskApplicationRepresentation
Degree of formalityHighly informal: in natural languageSemi-informal: in a restricted and structured form of natural languageSemi-formal: in an artificial and formally defined languageRigorously formal: in a language with formal semantics, theorems and proofs of such properties as soundness and completeness
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Languages for building ontologies
Ontologies can be built using various languages with variousdegrees of formality
Natural languageUMLEROWL/RDFSWSMLFOL...
The formalism and the language have an influence on the kind of knowledge that can be represented, and inferred
A conceptual model is not necessarily a formal ontology only because it is written in OWL
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Are ontologies just UML?
Ontologies vs ER schemasSemantic Web ontologies represented in Web-compatible languages, use Web technologiesThey represent a shared view over a domain
Ontologies vs UML diagramsFormal semantics of ontology languages defined, languages with feasible computational complexity available
Ontologies vs thesauriFormal semantics, domain-specific relationships
Ontologies vs taxonomiesRicher property types, formal semantics of the is-a relationship
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Did Linked Data kill ontologies?
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Ontologies in the age of Linked Data Publication according to LinkedData principles
Trade-off betweenacceptance/ease-of-use andexpressivity/usefulness
Human vs machine-orientedconsumption (using specifictechnologies)
Stronger commitment to reuseinstead of development from scratch
Model pre-defined through the(semi-) structure of the data to bepublished
Emphasis on alignment, especiallyat the instance level
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HOW TO BUILD A VOCABULARY
ONTOLOGY ENGINEERING
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Methodologies
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 systems
The On-To-Knowledge methodology takes a pragmatic approach to ontology engineering and contains many useful tips to support non-experts to build an ontology.
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
Source: Sure, 2003.
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Methodologies related to Software Engineering
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 acquisitionSource: METHONTOLOGY, Gómez-Pérez, A. ,1996.
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Collaborative methodologies
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
Source: DILIGENT: Tempich, 2006.
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Newer approaches
Ontology engineering increasingly becomes an community activity.
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.Less suitable for
developing anything that is not on a
mainstream topic
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.
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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
Condensed version
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Issues to be considered
What is the ontology going to be used for?
Who will use the ontology?
How it will be maintained and by whom?
What kind of data items will refer to it? And how will these references be created and maintained?
Are there any information sources available that could be reused?
To answer these questions, talk to domain experts, users, and software designers.
Domain experts don‘t need to be technical, they need to know about the domain, and help you understand its subtleties Users teach you about the terminology that is actually used and the information needs they have. Software designers tell you tell you about the type of use cases you need to handle, including the data to be described via the ontology
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Example: BBC
Various micro-sites built andmaintained manually
No integration across sites in terms of content and metadata
Use casesFind and explore content on specific (and related) topicsMaintain and re-organize sitesLeverage external resources
Ontology: One page per thing, reusing DBpedia andMusicBrainz IDs, different labels…
„Design for a world where Google is yourhomepage, Wikipedia is your CMS, and
humans, software developers andmachines are your users“
http://www.slideshare.net/reduxd/beyond-the-polar-bear
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REUSING EXISTINGKNOWLEDGE
Please stop building new ontologies…
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Where to find ontologiesSwoogle: over 10 000 documents, across domains
http://swoogle.umbc.edu/
Protégé Ontologies: several hundreds of ontologies, across domainshttp://protegewiki.stanford.edu/index.php/Protege_Ontology_Library#OWL_ontologies
Open Ontology Repository: work in progress, life sciences, but also other domainshttp://ontolog.cim3.net/cgi-bin/wiki.pl?OpenOntologyRepository
Tones: 218 ontologies, life sciences and core ontologies.http://owl.cs.manchester.ac.uk/repository/browser
Watson: several tens of thousands of documents, across domainshttp://watson.kmi.open.ac.uk/Overview.html
Talis repositoryhttp://schemacache.test.talis.com/Schemas/
Ontology Yellow Pages: around 100 ontologies, across domainshttp://wg.sti2.org/semtech-onto/index.php/The_Ontology_Yellow_Pages
OBO Foundation Ontologieshttp://www.obofoundry.org/
AIM@SHAPEhttp://dsw.aimatshape.net/tutorials/ont-intro.jsp
VoCampshttp://vocamp.org/wiki/Main_Page
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Swoogle functionality
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Swoogle coverage
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Protégé ontology library
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Open ontology repository
Presentation:http://ontolog.cim3.net/file/work/OOR/OOR_presentations_publications/OOR-SemTech_Jun2010.pdf
Demo: http://oor-01.cim3.net/search
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OBO Foundation ontologies
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More resources
http://vocamp.org/wiki/Where_to_find_vocabularies
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How to select the right ontology
What will the ontology be used for?Does it need a natural language interface and if yes in which language?Do you have any knowledge representation constraints (language, reasoning)?What level of expressivity is required?What level of granularity is required?
What will you reuse from it?Vocabulary++
How will you reuse it?Imports: transitive dependency between ontologies Changes in imported ontologies can result in inconsistencies and changes of meanings and interpretations, as well as computational aspects.
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How to select the right ontology (2)
The FOAF level: Use the simple ones, especially if they are used by othersas well
FOAF, DC, Freebase schemas…
The upper-level: Use upper-level ontologies, they are typically the result ofextensive discussions and considerations and allow you to ground yourmore specific ontologies
Other knowledge structures: Use taxonomies, vocabularies andfolksonomies as a baseline, but encode using Semantic Web languages
(Make your results available to the community)
Ontology learning: Apply existing tools to create a baseline structure, thenrevise and enrich
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WordNet http://www.w3.org/TR/wordnet-rdf/
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Freebase
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Freebase (ii)
Schemas: concepts/types, properties and instances, similar to ontologies.
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DBpedia
Extract structured information from Wikipedia and to make this information available on the Web
2.9 million things, 282,000 persons, 339,000 places (including 241,000 populated places), 88,000 music albums, 44,000 films, 15,000 video games, 119,000 organizations (including 20,000 companies and 29,000 educational institutions), 130,000 species, 4400 diseases
Ontology backbone259 classes arranged in a subsumption hierarchy with altogether 1200 propertiesOverview of all classes athttp://mappings.dbpedia.org/server/ontology/classesInfobox-to-ontology and the table-to-ontology mappings
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GoodRelations
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Other approaches
Create RDF data from existing resourceshttp://simile.mit.edu/wiki/RDFizershttp://esw.w3.org/ConverterToRdfSchema mappings have to be configured manually.Some issues to be considered
Open vs closed world assumptionSemantics of the is-a relationshipExpressivity: n-ary relatioships, attributes of relatotionships…
Enrich folksonomies: ambiguities, spelling variants and errors, abbreviations, multilinguality…
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Ontology engineering today
Various domains and application scenarios: life sciences, eCommerce, Linked Open Data
Engineering by reuse for most domains based on existing data andvocabularies
Alignment of data setsData curationHuman-aided computation (e.g., games, crowdsourcing)
Most of them much simpler and easier to understand than the often citedexamples from the 90s
However, still difficult to use (e.g., for mark-up)
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Ontology engineering today (2)
Back to the BBC example
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Ontology engineering today (3)
Scheduling
Control
Qualityassurance
Management
Configurationmanagement
Knowledge acquisition
Evaluation
Documentation
Support
Integration
Specification Conceptualization
Formalization Implementation
Maintenance
Development oriented
Pre-development
Development
Post-development
Merging
Environment study Feasibility study
Use Alignment
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Open topics
Meanwhile we have a better understanding of the scenarios which benefitfrom the usage of semantics and the technologies they typically deploy.
Guidelines and how-to‘sDesign principles and patternsSchema-level alignment (data-driven)Vocabulary evolutionAssessment and evaluation
Large-scale approaches to knowledge elicitation based on combinations ofhuman and computational intelligence.
www.kit.edu
Modeling hands-on
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Design principles
AbstractionIgnoring certain aspects in order to simplify the handling of something or to better understand other aspectsThe modeler decides what it is important or not and then chooses a representation that is more tractable than the originalA representation of something cannot be greater than that something
Models should be divisible
Model modules should be highly cohesive and have low coupling
Use informative labels
55
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The very basics
Some important thing Other important thingrelationship
The node is a non-trivial thing, easy to find in the domain, with a technological equivalent, with high cohesion and low couplingCandidates for nodes:
things or entities in ER models, knowledge bases classes in OO models typesstates in state machine diagrams etc
Relationships/associations/relations/properties/attributes hold between instances of the entities.Constraints/axioms/restrictions/rules further specify the nature of relationships.
constraint
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Classes
A class represents a set of instances
A class should be highly cohesive, precisely nameable, relevant
A class should have a strong identity
Crime Suspect
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How to find classes
Typical candidates: NOUNSActors of use cases do not necessarily correspond to classes
Other terms as wellVerbs
An association which starts to take on attributes and associations of its own turns into an entity: „Officer arrests suspect“Events: „Being ill“ „Illness episode“Passive form: re-formulate in active form
No pronouns
58
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Cohesion and identity
A class should represent one thing, all of that thing and nothing but thatthing
You can prove cohesion by Giving the class a representative nameNoun (+ adjective, sometimes however also captured as attribute value)
Blackmail victim, robbery victimBlue car, red carCars is not cohesive
Avoid ambiguous termsManager, handler, processor, list, information, item, data…
Identity ~ individuality: classes change values, but are still the same entityChild/Adult: age
59
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Relevance
Goint out too far vs. going down too far
Investigate homonyms and synonimsCan medicine and drug be considered synonims?Do they have the same properties/characteristics/attributes/relationships?Do they have a critical mass of commonalities?
60
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Characterizing classes
Two types of principal characteristicsMeasurable properties: attributesInter-entity connections: relationships, associations
Arrest details as attribute of the suspect vs. Arrest as a class vs Arrest as a relationship
Do we measure degrees of arrestedness or do we want to be able to distinguish between arrests?
Color of an image as attribute vs. class
A „pointing finger“ rather than a „ruler“ indicates identity
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Attributes
An attribute is a measurable property of a classScalar values: choice from a range of possibilitiesAn attribute is NOT a data structure. It is not complicated to measure
Value of attributes: integer, real numbers, enumerations, text…
Attributes do NOT exhibit identity
Attributes should have precise representative names
62
name:textage: integereyesight: enum{…}
Witness
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How to find attributes
Nouns in „-ness“Velocity-ness, job-ness, arrested-ness…
„How much, how many“ test.If you evaluate this, then it is probably an attributeIf you enumerate these, it is probably a class
Range of attributesAge abstracted as an integerLatitude and longitude: real numbers/NSEWNames abstracted as text
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Relationships
64
Crime Suspect
Crimecopycat
*
1
Person Vehicle0..1 0..*
Crime Officer* *
investigates
Some instancesof a class hold a relationship withsome instancesof another class.
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How to find relationships
Verbs, verbal phrases and things that could have been verbs. „The butler murdered the duchess“
Propertiesreflexivity, cardinality, functional, inverse-functional, discountinuousmultiplicity, many-to-many, all values from, some values of, transitivity, symmetry etc.
RolesNouns, adjectives.Verbs: indication of time‘s passing.
Short-term, one-to-one associations should be named with present participles.Longer-term, one-to-many associations should be named with past participles, or with the simple present third-person singular.
65
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Examples
Crime Officer* *
investigated
Crime Officer* *
investigating
Crime Officer* *investigating
is investigated
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Is-a hierarchy
Top-down, bottom-up, middle-out
Are all instances of entity A also instances of entity B?
Are all A‘s also B‘s?
Roles
Difference between classifications, associations, and aggregations
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Examples
Bill MealOrder
Dish Menu
Bed Mattress
Diary Appointment
Crime CrimeScene
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Overloadingsubsumption
InstantiationThing vs model
CompositionIs-a vs part-of
ConstitutionThing vs what matter is it made of
Examples due to Chris Welty, IBM Research
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Assignment: Modeling
“San Francisco Opera is the second largest opera company in North America. Gaetano Merola and Kurt Herbert Adler were the Company’s first two general directors. Merola led the Company from its founding in 1923 until his death in 1953; Adler was in charge from 1953 through 1981. Legendary for both their conducting and managerial skills, the two leaders established a formidable institution that is internationally recognized as one of the top opera companies in the world—heralded for its first-rate productions and roster of international opera stars. Following Adler’s tenure, the Company was headed by three visionary leaders: Terence A. McEwen (1982–1988), Lotfi Mansouri(1988–2001), and Pamela Rosenberg (2001–2005). Originally presented over two weeks, the Company’s season now contains approximately seventy-five performances of ten operas between September and July. San Francisco Opera celebrated the 75th anniversary of its performing home, the War Memorial Opera House, in 2007 . The venerable beaux arts building was inaugurated on October 15, 1932 and holds the distinction of being the first American opera house that was not built by and for a small group of wealthy patrons; the funding came thanks to a group of private citizens who encouraged thousands of San Franciscans to subscribe. The War Memorial currently welcomes some 500,000 patrons annually.”
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Assignment: Encoding in OWL
Fromhttp://www.jfsowa.com/ontology/
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Assignment: Alignment
The aim is to reach a ‚shared conceptualization‘ of all participants at theESWC2011 summer school on the ontology developed in the previousassigment.
Assumption: every group is committed to their conceptualization.Procedure: each group selects a representative, representativesagree on an editor, and on the actual steps to be followed.