development of ontologies guus schreiber swi, university of amsterdam co-chair w3c web ontology...

Development of Ontologies

Guus Schreiber

SWI, University of AmsterdamCo-chair W3C Web Ontology Working

Group

COST G9 workshop, 11 Oct 2002

2

Overview

• The notion of ontology• Ontology types and examples• Ontology languages• Ontology engineering: methods and tools


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What is an Ontology?

• In philosophy: theory of what exists in the world

• In IT: consensual & formal description consensual & formal description of shared concepts in a domainof shared concepts in a domain

• Aid to human communication and shared understanding, by specifying meaning

• Machine-processable (e.g., agents use ontologies in communication)

• Ontology = key technology in Ontology = key technology in semantic information processingsemantic information processing

• Applications: knowledge management, e-business, industrial engineering, semantic world-wide web


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What is an Ontology? (2)

Source: Financial Times, e-procurement, Oct. 2000


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The notion of ontology

• Ontology = explicit specification of a shared

conceptualization that holds in a particular context”

(several authors)

• Captures a viewpoint an a domain: – Taxonomies of species– Physical, functional, & behavioral system descriptions– Task perspective: instruction, planning

• Main difference with data models is not the content, but the purpose (generalizes over applications)


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Ontology should allow for “representational promiscuity”

ontology

parameterconstraint -expression

knowledge base A

cab.weight + safety.weight = car.weight:

cab.weight < 500:

knowledge base B

parameter(cab.weight)parameter(safety.weight)parameter(car.weight)constraint-expression(

cab.weight + safety.weight = car.weight)constraint-expression(

cab.weight < 500)

rewritten as

viewpointmapping rules


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Ship design: STEP product model used for data exchange

Private ProgramRepresentation

Design System

“WriteDesign”

AssessmentReport

kactus

SubmittedDesign

“RetrieveDeign Markups”

NeutralFormat

Ship Builder/Designer

API‘C’

Void

Private ProgramRepresentation

AssessmentSystem

kactus

Lloyd’s Register

API PrologVoid

“WriteDeign Markups”

“RetrieveDesign”

SPF

PrologInstances

Void

Express CMLSPF

KACTUSAP218

AssessmentOntology

ExpressInstances

?InstanceMappings

API Prolog

CML WorldEXPRESS World


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The importance of context

Principle 1: “The representation of real-world objects always

depends on the context in which the object is used. This context can be seen as a “viewpoint” taken on the object. It is usually impossible to enumerate in advance all the possible useful viewpoints on (a class of ) objects.”

Principle 2: “Reuse of some piece of information requires an explicit

description of the viewpoints that are inherently present in the information. Otherwise, there is no way of knowing whether, and why this piece of information is applicable in a new application setting.”


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Multiple views on a domain

• typical viewpoints captured in ontologies: – physical, functional, behavioral, process type: flow, energy,

..

• viewpoints typically overlap• applications require combinations of viewpoints

Heat Exchanger

platform design diagnosisprocess

simulation

physical structureconnections

mathematics ofheat exchange process

temparatruredifferences


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Ontology as conceptual structuring: multiple viewpoints & abstraction levels

• viewpoint decomposition• shape, geometry• function• behavior• causality• structure: part-of (mereology), aggregation • connectedness (topology)

• abstraction (generalization) level organization:• Intel 166 MHz• micro-processor• device component• (sub)system: part-of, connectedness• thing


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Leveling of ontologies

• Ontologies can have a recursive structure:• One ontology expresses a viewpoint on

another ontology.• Entails a reformulation and/or reinterpretation

of the underlying domain theories.• Often used to specify increasingly application-

specific interpretations and/or reformulations of domain expressions.

• Notion of ontology mapping– Still poorly understood


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Multiple ontology levels

x + y = z0 < z < 10

constraint-expression(x + y = z)constraint-expression(0 < z < 10)

calculation(x + y = z) constraint(0 < z < 10)

Interpreted as a numerical/logicaldependency between system parameters

Interpretation according to role in the problem solving process


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Context specification through ontology types

• Domain-specific ontologies– Medicine: UMLS, SNOMED, Galen– Art history: AAT, ULAN– STEP application protocols

• Task-specific ontologies– Classification

– E-commerce

• Generic ontologies • Top-level categories• Units and dimensions


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Art and Architecture Thesaurus


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Domain ontology of a traffic light control system


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Classification ontology

descriptionuniverse

descriptiondimension

descriptor

value set

value

descriptorvalue

object

object type object class

classconstraint

has feature

descriptorvalue set

in dimension

instance of

class of

hasdescriptor

1+

1+

1+

1+

1+

1+


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Ontology for e-commerce


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Top-level categories:many different proposals

Chandrasekaran et al. (1999)


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Ontology specification

• Many different languages– KIF– Ontolingua– Express – LOOM– UML– RDF Schema / DAML+OIL / OWL

• Common basis– Class (concept)– Subclass with inheritance– Relation (slot)


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Additional expressivity (1 of 2)

• Multiple subclasses• Aggregation

– Built-in part-whole representation

• Relation-attribute distinction– “Attribute” is a relation/slot that points to a data type

• Treating relations as classes– Sub relations– Reified relations (e.g., UML “association class”)

• Constraint language


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Additional expressivity (2 of 2)

• Class/subclass semantics– Primitive vs. defined classes– Complete/partial, disjoint/overlapping subclasses

• Set of basic data types• Modularity

– Import/export of an ontology

• Ontology mapping– Renaming ontological elements– Transforming ontological elements

• Sloppy class/instance distinction– Class-level attributes/relations– Meta classes


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Priority list for expressivity

• Depends on goal:– Deductive capability: “limit to subset of first-order

logic”– Maximal content: “as much as (pragmatically)

possible”

• My priority list (from a “maximal-content”

representative)1. Multiple subclasses2. Reified relations3. Import/export mechanism4. Sloppy class/instance distinction5. Aggregation6. Constraint language


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Expressivity of RDF Schema

• Class– Describes collection of resources

• Property– Links class to another class or to a “literal” (data

value)– Domain and range restrictions

• Subclass relation– Property inheritance

• Subproperty relation• Classes and properties are themselves also

resources– Cf. “classes as instances”


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OWL: W3C Web Ontology Language• Basis = RDF Schema• Basic features (OWL Lite/Core):

– Cardinality restrictions (limited)– Local range constraints– Equality of resources– Inverse, symmetric and transitive properties– Datatypes (reference to XML Schema)

• Advanced features (OWL DL) – Boolean class combinations– Disjointness and completeness– Nameless classes– Cardinality restrictions (full)

• Under development, see http://www.w3.org


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Example UML presentation of OWL


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Modelling issue:classes as instances

Aircraft-type

no-of-engines: integer >0propulsion: {propeller,

jet}

Fokker-70

instance of Aircraft-typeno-of-engines = 2

propulsion = jet

Aircraft

no-of-seats: positive integer

owner: Airline

Fokker-70

subclass of Aircraft

no-of-seats: 60-80

PH-851

instance of Fokker-70

no-of-seats = 65

owner = KLM


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Modelling issuedefinitional and default knowledge

IF style/period = “Late Georgian”THEN (by definition) culture = “British” AND date.created between 1760-1811

IF type = “chest of drawers” style/period = “Late Georgian”THEN (this typically suggests) material.main = “mahogany”


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Modelling issue:dealing with existing hierarchies

<color>

<chromatic color>

pink

vivid pink

strong pink

<intermediate pink>

purplish pink

brilliant purplish pink

yellowish pink

<neutral color>


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Limitations of Hierarchies

• What’s in a link? – Hierarchical links often have different semantics

• “Dimensions” of distinction making provide rationale for hierarchical levels– (Multiple) classification along different dimensions

within single hierarchy creates confusion and makes applications unnecessarily complex

• Hierarchy enforces a single fixed sequence of dimensions– fixed ordering not always possible or desirable


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Two different organizations of the disease hierarchy

infection

meningitis pneumonia

bacterialpneumonia

acute viralpneumonia

chronic viralpneumonia

viralpneumonia

infection

meningitis pneumonia

chronicpneumonia

acute viralpneumonia

acute bacterialpneumonia

acutepneumonia


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Characteristics of ontologies: viewpoints - simultaneous multiple classifications

infection

acuteinfection

chronicinfection

viralinfection

bacterialinfection

meningitispneumonia

acute viralmeningitis

causal agenttime factor

Note: different dimensions along which distinctions are made (e.g. time, location, cause,…) often occur and are used simultaneously in a task.


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Modelling issue:part-whole relation • Examples:

– a wing spar is part of a wing assembly

– chests of drawers have feet with their own style

• Most items in collections have some internal structure


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Part-whole relations

• Important for describing objects with structure

• Semantics are complicated• Different type of part-whole relations can be

distinguished• Good overview article:

– A. Artale, E. Franconi, N. Guarino and L. Pazzi. Part-Whole Relations in Object-Centered Systems: An Overview. Data and Knowledge Engineering. October 1996


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WCH typology of part-whole relations• Three features

– Do the parts play a functional role in the whole?– Is the part made of the same thing as the whole?– Can the parts be separated from the whole?

• Component / Integral object– Example: elevator, car– Functional, separable, non homegenous

• Member / Collection– Idem, but non-functional (tree in forest)

• Portion / Mass– Separable, homogenous (slice of bread)

• Place / Area– Not separable, homogenous

(Lunteren part-of Gelderland)

• Stuff / Object– Not separable, not homogenous (steel in bike)


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Modelling of part-whole relations• Explicit introduction of wholes• Distinction between parts and other featues

(attributes, relations) of the whole• Built-in transitivity of parts

– If A part-of B and B part-of C then A part-of C

• Generic names for parts– Typically describe functional roles (car has wheels)

• Vertical relationships– Existence dependency between whole and part– Feature dependencies:

• Inheritance from part to whole: “defective”• Inheritance from whole to part: “owner”• Systematic relation: weight whole = sum weight parts

• Horizontal relationships– Constraints between parts


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Ontology mappings

animal description

gographical locationterrain type

species

geographical rangetypical habitats

animal+image description ontology

speciesontology

WordNet

geographical location continent Asia country Indonesia regio J ava city Bandung

terrain type rain forest savanna pampa tundra taiga

geographicalstandard


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Guidelines for ontological engineering (1)• Do not develop from scratch• Use existing data models and domain standards

as starting point• Start with constructing an ontology of common

concepts• If many data models, start with two typical ones• Make the purpose and context of the ontology

explicit– E.g. data exchange between ship designers and

assessors– Operationalize purpose/context with use cases

• Use multiple hierarchies to express different viewpoints on classes

• Consider treating central relationships as classes


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Guidelines for ontological engineering (2)• Do not confuse terms and concepts• Small ontologies are fine, as long as they meet

their goal• Don’t be overly ambitious: complete unified

models are difficult• Ontologies represent static aspects of a domain

– Do not include work flow

• Use a standard representation format, preferably with a possibility for graphical representation

• Decide about the abtraction level of the ontology early on in the process.– E.g., ontology only as meta model


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Ontology tools

Some well known tools• Protégé (Stanford)• OntoEdit (now: OI Modeller / KAON)• OilEd (Manchester)

Decision points:– Expressivity– Graphical representation– DB backend– Modularization support– Versioning


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Small ontology construction example

Source: M. Fowler, “Analysis Patterns”Translated into UML

Goal: conceptual model for observations in medical practice


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A simple representation


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The notion of quantity

John has a height of 185(unit = cm)


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Unit conversion

Inches can be converted into centimeters by multiplying with 2.54Degrees Celsius can be converted into Fahrenheit with the formula F = 32 + 9C/5


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Introducing phenomena types

For John (person) a height (phenomena type)with a quantity of 185 (unit = cm) was measured on 11/11/2000 15:43 (time stamp)


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Qualitative observations

• Qualitative observation:” “category”• Example: John has blood group A• “Blood group” is a phenomenon type• “Blood group A” is a phenomenon• The fact “Blood group A” is present for John is

a category observation


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Qualitative and quantitative observations


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Observation method and observer

Dr. Smith has observed the height ofJohn by means of a length pole


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Resources

• Web portals– www.ontoweb.irg– www.semanticweb.org

• Articles, books on modelling:– T. R. Gruber, Towards principles for the design of

ontologies used for knowledge sharing, In: N. Guarino and R. Poli (eds.) Formal Ontology in Conceptual Analysis and

Knowledge Representation. Boston, Kluwer, 1994,– J. Martin & J. Odell, Object-Oriented Methods -- A

Foundation. UML edition, Upper Saddle River, NJ, Prentice Hall,, 1997

– M. Fowler, Analysis Patterns: Reusable Object Models Menlo Park, CA, Addison-Wesley, 1997.

development of ontologies guus schreiber swi, university of amsterdam co-chair w3c web ontology...

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