Eswcsummerschool2010 ontologies final

www.kit.edu

Vocabulary building (and alignment)

Elena Simperl

[email protected]

KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH)2

A LITTLE HISTORY


ontology vocabularymicroformat conceptual graph

topic map thesaurusschema

classification object model

semantic network

glossary taxonomy


Focus on knowledgerepresentation andreasoning

Academic topic

Research prototypesof ontology-based *

Standardization


Focus on dataintegration, community-driveninitiative on datapublishing

Community ofdevelopers anddata and contentproviders

Leveragingmaturing semantictechnologies, andother trends (open access)


It was never a simple matter

What exists?

What is?

What am I?

Ontologies and the Semantic Web / Ontologies - A Brief History - 6


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


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


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


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)))


Make domain assumptions explicit


Separate domain and operational knowledge


ONTOLOGIES AND SEMANTICTECHNOLOGIES


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)


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


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.


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.


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…


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.


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


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


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


Did Linked Data kill ontologies?


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


HOW TO BUILD A VOCABULARY

ONTOLOGY ENGINEERING


Methodologies

Enterprise Ontology[Uschold & King, 1995]

IDEF5[Benjamin et al. 1994]

CO4[Euzenat, 1995]

CommonKADS[Schreiber et al., 1999]

Holsapple&Joshi[Holsapple & Joshi, 2002]


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.


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.


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.


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.


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


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


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

http://www.slideshare.net/reduxd/beyond-the-polar-bear�


REUSING EXISTINGKNOWLEDGE

Please stop building new ontologies…


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

http://swoogle.umbc.edu/�

http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library�

http://ontolog.cim3.net/cgi-bin/wiki.pl?OpenOntologyRepository�

http://owl.cs.manchester.ac.uk/repository/browser�

http://watson.kmi.open.ac.uk/Overview.html�

http://schemacache.test.talis.com/Schemas/�

http://wg.sti2.org/semtech-onto/index.php/The_Ontology_Yellow_Pages�

http://www.obofoundry.org/�

http://dsw.aimatshape.net/tutorials/ont-intro.jsp�

http://vocamp.org/wiki/Main_Page�


Swoogle functionality


Swoogle coverage


Protégé ontology library


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

http://ontolog.cim3.net/file/work/OOR/OOR_presentations_publications/OOR-SemTech_Jun2010.pdf�



http://oor-01.cim3.net/search�


OBO Foundation ontologies


More resources

http://vocamp.org/wiki/Where_to_find_vocabularies

http://vocamp.org/wiki/Where_to_find_vocabularies�


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.


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


WordNet http://www.w3.org/TR/wordnet-rdf/

http://www.w3.org/TR/wordnet-rdf/�




Freebase


Freebase (ii)

Schemas: concepts/types, properties and instances, similar to ontologies.


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

http://mappings.dbpedia.org/server/ontology/classes�


GoodRelations


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…

http://simile.mit.edu/wiki/RDFizers�

http://esw.w3.org/ConverterToRdf�


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)


Ontology engineering today (2)

Back to the BBC example


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


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


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


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


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


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


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


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


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

61


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


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


Relationships

64

Crime Suspect

Crimecopycat

*

1

Person Vehicle0..1 0..*

Crime Officer* *

investigates

Some instancesof a class hold a relationship withsome instancesof another class.


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


Examples

Crime Officer* *

investigated

Crime Officer* *

investigating

Crime Officer* *investigating

is investigated


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


Examples

Bill MealOrder

Dish Menu

Bed Mattress

Diary Appointment

Crime CrimeScene


Overloadingsubsumption

InstantiationThing vs model

CompositionIs-a vs part-of

ConstitutionThing vs what matter is it made of

Examples due to Chris Welty, IBM Research


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.”


Assignment: Encoding in OWL

Fromhttp://www.jfsowa.com/ontology/

http://www.jfsowa.com/ontology/�


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.

Sports

Eswcsummerschool2010 ontologies final