Development of Ontologies

1
Development of Ontologies

• Guus Schreiber
• SWI, University of Amsterdam
• Co-chair W3C Web Ontology Working Group

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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 of 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 semantic 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
parameter
constraint -expression
mapping rules
viewpoint
knowledge base B
knowledge base A
parameter(cab.weight)
parameter(safety.weight)
cab.weight safety.weight
parameter(car.weight)
rewritten as
car.weight
constraint-expression(
cab.weight safety.weight
cab.weight lt 500
car.weight)
constraint-expression(
cab.weight lt 500)
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Ship design STEP product model used for data
exchange
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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

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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
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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
description
object
universe
instance of
1
1
description
dimension
class of
object type
object class
in dimension
1
value set
1
1
has
descriptor
descriptor
descriptor
value set
descriptor
1
value
has feature
value
class
constraint
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Ontology for e-commerce
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Top-level categoriesmany different proposals
Chandrasekaran et al. (1999)
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Ontology specification

• Many different languages
• KIF
• Ontolingua
• Express
• LOOM
• UML
• RDF Schema / DAMLOIL / 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)
• Multiple subclasses
• Reified relations
• Import/export mechanism
• Sloppy class/instance distinction
• Aggregation
• 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 issueclasses as instances
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

• Aircraft-type
• no-of-engines integer gt0
• propulsion propeller, jet
• Fokker-70
• instance of Aircraft-type
• no-of-engines 2
• propulsion jet

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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 issuedealing with existing hierarchies

• ltcolorgt
• ltchromatic colorgt
• pink
• vivid pink
• strong pink
• ltintermediate pinkgt
• purplish pink
• brilliant purplish pink
• yellowish pink
• ltneutral colorgt

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Limitations of Hierarchies

• Whats 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
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Characteristics of ontologies viewpoints -
simultaneous multiple classifications
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 issuepart-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
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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
• Dont 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 2.54 Degrees 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 1543 (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 of John 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.