Understanding Ontologies

1
Understanding Ontologies
2
What is an Ontology?

• An ontology defines the common words and concepts
  (the meaning) used to describe and represent an
  area of knowledge
• An ontology is a specification of the
  conceptualization of a term
• An ontology models the vocabulary and meaning of
  domains of interests in a computer-usable form ?
  make knowledge reusable

3
Ontology Spectrum
Thesauruses
Formalis-a
Frames(properties)
GeneralLogicalconstraints
narrower termrelation
Catalog/ID
Formalinstance
Disjointness,Inverse,Part-of
Terms/glossary
Informalis-a
ValueRestriction
4
Ontology Spectrum (Cont.)

• Controlled vocabulary a finite list of terms
• Glossary a list of terms and meanings (NL
  statements)
• Would not meet the requirement of being
  machine-processible
• Thesaurus additional semantics in their
  treatments of relations between terms
• synonym, hyponym, narrower, broader
• No explicit hierarchy
• Term hierarchies Yahoos catalog
• Semantically weaker taxonomies
• Without true subclass (is-a) relationships,
  certain kinds of deductive uses of ontologies
  become problematic

5
Ontology Spectrum (Cont.)

• Strict subclass hierarchies
• If A is a superclass of B, then if an object is
  an instance of B, it necessarily follows that the
  object is an instance of A
• Necessary for exploitation of inheritance
• Formal instance relationships
• Some concept classification schemes include only
  class names, whereas others include ground
  individual content, including instances of
  classes
• Frame (property information)
• Useful for knowledge modeling when they are
  specified at a general class level and inherited
  consistently by subclasses and instances

6
Ontology Spectrum (Cont.)

• Value restrictions
• Place restrictions on what can fill a property
• Integer, range
• Arbitrary logical statements
• Ex. The value of one property based on other
  properties
• First-order logic constraints, disjoint classes,
  part-whole relationship

7
What is an Ontology?

• Mandatory
• Finite controlled (extensible) vocabulary
• Unambiguous interpretation of classes and term
  relationships
• Strict hierarchical subclass relationships
  between classes
• Typical
• Property specification on a per-class basis
• Individual inclusion in the ontology
• Value restriction specification on a per-class
  basis
• Desirable
• Specification of disjoint classes
• Specification of arbitrary logical relationships
  between terms
• Distinguished relationships, such as inverse and
  part-whole

8
What is an Ontology?

• An ontology defines the common words and concepts
  (the meaning) used to describe and represent an
  area of knowledge
• An ontology models the vocabulary and meaning of
  domains of interests in a computer-usable form ?
  make knowledge reusable
• Classes (general things, objects) in domains
• Instances (particular things)
• The relationships among those things
• The properties (and properties values) of those
  things
• The functions of and processes involving those
  things
• Constraints on and rules involving those things

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Graphical Ontology ExampleHuman Resources
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Ontology-Related Fields

• Knowledge engineering
• Knowledge representation
• Qualitative modeling
• Language engineering
• Database design
• Information retrieval and extraction
• Knowledge management and organization
• Library science
• Ontology-enhanced search
• E-commerce
• Configuration

11
SW and Ontology

• In SW, information is given explicit meaning,
  making it easier for machines to automatically
  process and integrate information available on
  the Web.
• XML define customized tagging schemes (lack of
  semantics)
• RDF/RDFS represent data
• The next element required for the SW is a web
  ontology language which can formally describe the
  semantics of classes and properties used in web
  documents.
• In order for machines to perform useful reasoning
  tasks on these documents, the language must go
  beyond the basic semantics of RDF Schema

12
SW and Ontology (Cont.)

• In SW, Ontologies is a way of representing the
  semantics of documents and enabling the semantics
  to be used by web applications and intelligent
  agents.
• Ontologies can prove very useful for a community
  as a way of structuring and defining the meaning
  of the metadata terms that are currently being
  collected and standardized.
• Using ontologies, tomorrow's applications can be
  "intelligent," in the sense that they can more
  accurately work at the human conceptual level.

13
SW and Ontology (Cont.)

• In SW, Ontologies is a way of representing the
  semantics of documents and enabling the semantics
  to be used by web applications and intelligent
  agents.
• Ontologies can prove very useful for a community
  as a way of structuring and defining the meaning
  of the metadata terms that are currently being
  collected and standardized.
• Using ontologies, tomorrow's applications can be
  "intelligent," in the sense that they can more
  accurately work at the human conceptual level.

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Simple Ontologies and Their Uses
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Simple Ontologies Available

• DMOZ (Directory Mozilla) (http//www.dmoz.com)
• Unified medical language system (UMLS)
  (http//www.nlm.nih.gov/research/umls/)
• Cycorp (http//www.cyc.com)
• http//www.daml.org/ontologies/submitter.html

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Simple Ontologies Uses

• Controlled vocabulary
• Users, authors, and databases can all use (share)
  terms from the same vocabulary
• Common term usage is a start for interoperability
• Site organization and navigation support
• Expose the top levels of a generation hierarchy
  of terms as a kind of browsing structure
• Support expectation setting let users quickly
  determine the site might have content/service of
  interest to them
• Search support
• Content on a site may be tagged with terms from
  the ontology
• Search engines may exploit the tagging and
  provide enhanced search capability

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Simple Ontologies Uses (Cont.)

• Umbrella structures from which to extend
  content
• High-level taxonomic organization from which many
  efforts may inherit terms
• Universal Standard Products and Services
  Classification (UNSPSC) (http//www.unspsc.org)
• Provide the infrastructure for interoperability
  of terms in the domains of products and services
• A number of e-commerce applications are looking
  for umbrella organization structures (UNSPSC)
• May need to extend
• Share umbrella or upper-level ontology for
  interoperability
• Sense disambiguation support Jordan, Java

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A Portion of the UNSPEC Electronic Commerce
Taxonomy
Live plant and Animal
Live animals
Livestock
Subclass of
Cats
Dogs
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Structured Ontologies and Their Uses
Make application smarter

• Consistency checking
• Property type validation and value restrictions
• Provide completion
• High-resolution screen ? ONTOLOGY ? 1024768
• man ? ONTOLOGY ? male ? Dont ask him
  questions about pregnant
• Interoperability support
• Structured ontologies may have a complete
  operational definition for how on term relates to
  another term
• StandardEmployee a Person with employer
  StanfordUniv.
• Person, employer, StanfordUniv.

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Structured Ontologies and Their Uses (Cont.)

• Exploit generalization/specialization information
• If a search applications finds that a users
  query generates too many answers ?
  specialization
• Ex. Concerts in the San Francisco Bay Area
• Show subclasses of Concerts (like Rock,
  Classic)
• Provide alternative values (such as siblings)
• Support structured, comparative, and customized
  search
• Television ?Properties (diagonal, price,
  manufacturer)
• Provide a form including these properties for
  users to fill in
• Mark which properties are most useful to present
  in comparative analysis

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Structured Ontologies and Their Uses (Cont.)

• Support validation and verification testing of
  data (and schemas)
• If a Person can have only one employer ? some
  Person has 2 or 3?
• If a Person is an Employee ? employer should not
  be empty
• Chimaera Check ontologies for problems in
  ontology definition as well as problems with the
  instance data

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Use Cases of Web Ontologies
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Web Portals

• Provide information content on a common topic
• A starting place for locating interesting content
• Submitted by members of the community
• Often index it under some subtopic.
• Rely on the content providers tagging the content
  with information that can be used in syndicating
  it.
• Use simple metatags that identify the topic of
  the content
• Define an ontology for the community
• Provide a terminology for describing content and
  axioms that define terms using other terms from
  the ontology
• "journal paper," "publication," "person," and
  "author." "all journal papers are publications"
  or "the authors of all publications are people."

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Web Portals with Ontology

• When combined with facts, these definitions allow
  other facts that are necessarily true to be
  inferred
• These inferences can allow users to obtain search
  results from the portal that are impossible to
  obtain from conventional retrieval systems
• Rely on content providers using the web ontology
  language to capture high-quality ontology
  relationships
• Ontology-based Web Portals
• OntoWeb
• The Open Directory Project

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Multimedia Collection

• Use ontologies to provide semantic annotations
  for collections of images, audio, or other
  non-textual objects
• Ontologies would capture additional knowledge
  about the domain that can be used to improve
  retrieval of images
• Example archive of images of antique furniture
• Create a taxonomy to classify the different types
  of furniture
• "Late Georgian" ? date.created in 1760, 1811
  AND culture is British (benefit for Indexer and
  Searcher)
• "Late Georgian chest of drawers" ? made of
  mahogany
• A user query for "antique mahogany storage
  furniture" could match with images of Late
  Georgian chests of drawers, even if nothing is
  said about wood type in the image annotation.
• Real semantic query

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Corporate Web Site Management

• Corporations have numerous web pages concerning
  press releases, product offerings and case
  studies, white papers
• Ontologies can be used to index these documents
  and provide better means of retrieval
• A single ontology is often limiting because the
  constituent categories are likely constrained to
  those representing one view and one granularity
  of a domain
• The ability to simultaneously work with multiple
  ontologies would increase the richness of
  description

27
Corporate Web Site Management (Cont.)

• Useful for each class of user to have different
  ontologies of terms, but have each ontology
  interrelated so translations can be performed
  automatically
• A salesperson looking for sales collateral
  relevant to a sales pursuit.
• A technical person looking for pockets of
  specific technical expertise and detailed past
  experience.
• A project leader looking for past experience and
  templates to support a complex, multi-phase
  project, both during the proposal phase and
  during execution

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Corporate Web Site Management (Cont.)

• Frame queries at the right level of abstraction.
• Expertise in OS ? expert employee with both Unix
  and Windows
• Facilitate content reuse
• Use ontologies to reason about how past templates
  and documents can be reassembled in new
  configurations, while satisfying a diverse set of
  preconditions.

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Design Documentation

• Engineering documentation in aerospace industry
• Design, manufacturing, and testing documentation
• Each set has a hierarchical structure, but differ
  between sets
• Cross-linking a wing spar might appear in each
  design doc.
• Concrete example in aerospace
• ME looking for all information relating to a
  particular part
• DE looking at constraints on re-use of a
  particular sub-assembly
• Use constraints to enhance search or check
  consistency
• biplane(X) ? CardinalityOf(wing(X)) 2
• wingspar(X) AND wing(Y) AND isComponentOf(X,Y) ?
  length(X) lt length(Y)

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Design Documentation (Cont.)

• Support the visualization and editing of charts
• Show snapshots of the information space centered
  on a particular concept
• typically activity/rule diagrams or
  entity-relationship diagrams

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Agents and Services

• The SW can provide agents with the capability to
  understand and integrate diverse information
  resources.
• Example Social activities planner (take the
  preferences of a user to plan the user's
  activities)
• Depend upon the richness of the service
  environment being offered and the needs of the
  user.
• During the service determination/matching
  process, ratings and review services may also be
  consulted
• Require
• Domain ontologies that represent the terms for
  restaurants, hotels
• Service ontologies to represent the terms used in
  the actual services.

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Agents and Services (Cont.)

• Key issues
• Use and integration of multiple separate
  ontologies across different domains and services
• Distributed location of ontologies across the
  Internet
• Potentially different ontologies for each domain
  or service (ontology translation/cross-referencing
  )
• Simple ontology representation to make the task
  of defining and using ontologies easier
• Agentcities

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Ubiquitous Computing

• An emerging paradigm of personal computing
• Characterized by the shift from dedicated
  computing machinery to pervasive computing
  capabilities embedded in our everyday
  environments.
• Small, handheld, wireless computing devices.
• Require network architectures to support
  automatic, ad hoc configuration.
• "Serendipitous interoperability,"
  interoperability under "un-choreographed"
  conditions
• Service discovery, contracting, and composition

34
Ubiquitous Computing (Cont.)

• An ontology language will be used to describe the
  characteristics of devices, the means of access
  to such devices, the policy established by the
  owner for use of a device, and other technical
  constraints and requirements that affect
  incorporating a device into a ubiquitous
  computing network.
• DAML-S
• RDF-based schemes for representing information
  about device characteristics
• W3C's Composite Capability/Preference Profile
  (CC/PP)
• WAP Forum's User Agent Profile or UAProf)

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Ontology Acquisition

• Many ontologies exist in the public domain
• Ontology obtainment
• Start with an existing industry standard and use
  that as the ontology starting point
• Modify/extend to meet application-specific
  requirement
• Semi-automatically generate a starting point
• Crawl certain site or analyze documents to obtain
  a starting taxonomic structure, and then analyze,
  modify and extend it

36
Ontology Acquisition (Cont.)

• Ontology sources
• NIST (http//www.nist.gov)
• RosettaNet (http//www.rosettanet.org)
• IT, electronic technology, electronic components,
  semiconductor manufacturing
• Trade organizations or e-commerce site
• DARPA High Performance Knowledge Base Program and
  the Rapid Knowledge Formation Program
  (http//reliant.teknowledge.com/RKF)
• DAML ontologies (http//www.daml.org/ontologies)

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Ontology-Related Implications and Needs

• Ontology language
• Expressive power of a representation and
  reasoning
• Range check, property specification with value
  restriction
• If support disjoint ? inference engine must check
  and enforce
• Take the best of research on expressive power
  along with reasoning power, and provide
  representationally powerful languages that have
  known reasoning property and are efficient in
  their implementation
• Syntax description and semantic description
• DAML OIL ? OWL
• Merge the best of existing Web languages,
  description logics, and frame-based systems

38
Ontology-Related Implications and Needs (Cont.)

• Environment
• How to generate, analyze, modify, and maintain an
  ontology over time
• Ontology toolkits
• Verity, Ontolingua, Chimaera, OilEd, Protégé

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Protégé
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Ontology-Related Implications and Needs (Cont.)

• Environment (Cont.)
• Issues to be considered when use or build an
  ontology environment
• Collaboration and distributed workforce support
• Platform interconnectivity
• Scale
• Versioning
• Security
• Analysis
• Life cycle issues
• Ease of use
• Diverse use support
• Presentation style
• Extensibility

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OWL (Web Ontology Language)Overview
42
Overview

• W3C recommendations related to the Semantic Web
• XML provides a surface syntax for structured
  documents, but imposes no semantic constraints on
  the meaning of these documents.
• XML Schema is a language for restricting the
  structure of XML documents and also extends XML
  with datatypes.
• RDF is a datamodel for objects ("resources") and
  relations between them, provides a simple
  semantics for this datamodel, and these
  datamodels can be represented in an XML syntax.
• RDF Schema is a vocabulary for describing
  properties and classes of RDF resources, with a
  semantics for generalization-hierarchies of such
  properties and classes.
• OWL adds more vocabulary for describing
  properties and classes among others, relations
  between classes (e.g. disjointness), cardinality
  (e.g. "exactly one"), equality, richer typing of
  properties, characteristics of properties (e.g.
  symmetry), and enumerated classes.

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Overview (Cont.)

• The OWL Web Ontology Language is designed for use
  by applications that need to process the content
  of information instead of just presenting
  information to humans
• OWL is a revision of the DAMLOIL web ontology
  language

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The three sublanguages of OWL

• OWL Lite
• Supports a classification hierarchy and simple
  constraints.
• Supports cardinality constraints, but only
  permits 0 or 1.
• Provides a quick migration path for thesauri and
  other taxonomies
• OWL DL (description logics)
• Supports the maximum expressiveness while
  retaining computational completeness (all
  conclusions are guaranteed to be computable) and
  decidability (all computations will finish in
  finite time).
• OWL DL includes all OWL language constructs, but
  they can be used only under certain restrictions
  (for example, while a class may be a subclass of
  many classes, a class cannot be an instance of
  another class).

45
The three sublanguages of OWL (Cont.)

• OWL Full
• Support maximum expressiveness and the syntactic
  freedom of RDF with no computational guarantees.
  Examples
• A class can be treated simultaneously as a
  collection of individuals and as an individual in
  its own right.
• Attach property to classes
• OWL Full allows an ontology to augment the
  meaning of the pre-defined (RDF or OWL)
  vocabulary.
• It is unlikely that any reasoning software will
  be able to support complete reasoning for every
  feature of OWL Full.

46
The three sublanguages of OWL (Cont.)

• Each of these sublanguages is an extension of its
  simpler predecessor, both in what can be legally
  expressed and in what can be validly concluded
• Every legal OWL Lite ontology is a legal OWL DL
  ontology.
• Every legal OWL DL ontology is a legal OWL Full
  ontology.
• Every valid OWL Lite conclusion is a valid OWL DL
  conclusion.
• Every valid OWL DL conclusion is a valid OWL Full
  conclusion.
• OWL Full can be viewed as an extension of RDF,
  while OWL Lite and OWL DL can be viewed as
  extensions of a restricted view of RDF
• Every OWL (Lite, DL, Full) document is an RDF
  document, and every RDF document is an OWL Full
  document, but only some RDF documents will be a
  legal OWL Lite or OWL DL document.

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Language Synopsis
48
OWL Lite Synopsis

• RDF Schema Features
• Class (Thing, Nothing)
• rdfssubClassOf
• rdfProperty
• rdfssubPropertyOf
• rdfsdomain
• rdfsrange
• Individual

• (In)Equality
• equivalentClass
• equivalentProperty
• sameAs
• differentFrom
• AllDifferent
• distinctMembers

49
OWL Lite Synopsis (Cont.)

• Property Characteristics
• ObjectProperty
• DatatypeProperty
• inverseOf
• TransitiveProperty
• SymmetricProperty
• FunctionalProperty
• InverseFunctionalProperty
• Property Restrictions
• Restriction
• onProperty
• allValuesFrom
• someValuesFrom

• Restricted Cardinality
• minCardinality (only 0 or 1)
• maxCardinality (only 0 or 1)
• cardinality (only 0 or 1)
• Header Information
• Ontology
• imports
• Class Intersection
• intersectionOf

50
OWL Lite Synopsis (Cont.)

• Versioning
• versionInfo
• priorVersion
• backwardCompatibleWith
• incompatibleWith
• DeprecatedClass
• DeprecatedProperty

• Annotation Properties
• rdfslabel
• rdfscomment
• rdfsseeAlso
• rdfsisDefinedBy
• AnnotationProperty
• OntologyProperty
• Datatypes
• xsd datatypes

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OWL DL and Full Synopsis

• Class Axioms
• oneOf, dataRange
• disjointWith
• equivalentClass(applied to class expressions)
• rdfssubClassOf(applied to class expressions)
• Boolean Combinations of Class Expressions
• unionOf
• complementOf
• intersectionOf

• Arbitrary Cardinality
• minCardinality
• maxCardinality
• cardinality
• Filler Information
• hasValue

52
Language Description of OWL Lite
53
OWL Lite RDF Schema Features

• Class
• A class defines a group of individuals that
  belong together because they share some
  properties.
• Classes can be organized in a specialization
  hierarchy using subClassOf.
• Thing a superclass of all OWL classes.
• Nothing the class that has no instances and a
  subclass of all OWL classes.
• rdfssubClassOf
• Used to create class hierarchies
• Example Person is a subclass of Mammal
• A reasoner if an individual is a Person, then it
  is also a Mammal

54
OWL Lite RDF Schema Features (Cont.)

• rdfProperty
• Used to state relationships between individuals
  or from individuals to data values
• Examples hasChild, hasRelative, hasSibling, and
  hasAge
• ObjectProperty and DatatypeProperty
• Both owlObjectProperty and owlDatatypeProperty
  are subclasses of the RDF class rdfProperty
• rdfssubPropertyOf
• Used to create property hierarchies
• hasSibling is a subproperty of hasRelative

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OWL Lite RDF Schema Features (Cont.)

• rdfsdomain
• Limits the individuals to which the property can
  be applied.
• Example the property hasChild has the domain of
  Mammal.
• If Frank hasChild Anna, then Frank must be a
  Mammal.
• rdfsrange
• Limits the individuals that the property may have
  as its value.
• Example the property hasChild has the range of
  Mammal.
• If Deborah is the child of Louise, then Deborah
  is a Mammal.
• Individual
• Individuals are instances of classes, and
  properties may be used to relate one individual
  to another.

56
OWL Lite Equality and Inequality

• equivalentClass Two classes may be stated to be
  equivalent.
• Equivalent classes have the same instances.
• Equality can be used to create synonymous
  classes.
• Example Car is a equivalentClass to Automobile.
• Any individual that is an instance of Car is also
  an instance of Automobile and vice versa.
• equivalentProperty Two properties may be stated
  to be equivalent.
• Equivalent properties relate one individual to
  the same set of other individuals.
• Equality may be used to create synonymous
  properties.
• Example hasLeader is the equivalentProperty to
  hasHead.
• If X is related to Y by the property hasLeader, X
  is also related to Y by the property hasHead and
  vice versa.
• hasLeader is a subproperty of hasHead and hasHead
  is a subProperty of hasLeader.

57
OWL Lite Equality and Inequality (Cont.)

• sameAs Two individuals may be stated to be the
  same.
• Used to create a number of different names that
  refer to the same individual
• Example Deborah is the same individual as
  DeborahMcGuinness
• differentFrom
• An individual may be stated to be different from
  other individuals.
• Example Frank is different from Deborah and Jim.
  
• If Frank and Deborah are both values for a
  property that is stated to be functional ?
  contradiction
• AllDifferent
• A number of individuals may be stated to be
  mutually distinct in one AllDifferent statement.
• Example Frank, Deborah, and Jim are mutually
  distinct using the AllDifferent construct.
• Enforce that Jim and Deborah are distinct (not
  just that Frank is distinct from Deborah and
  Frank is distinct from Jim).

58
OWL Lite Property Characteristics

• inverseOf
• If the property P1 is inverse of the property P2,
  then if X is related to Y by the P2 property,
  then Y is related to X by the P1 property.
• Example if hasChild is the inverse of hasParent
  and Deborah hasParent Louise ?Louise hasChild
  Deborah
• TransitiveProperty
• If the pair (x,y) is an instance of the
  transitive property P, and the pair (y,z) is an
  instance of P, then the pair (x,z) is also an
  instance of P.
• Example if ancestor is stated to be transitive,
  and if Sara is an ancestor of Louise and Louise
  is an ancestor of Deborah ? Sara is an ancestor
  of Deborah

59
OWL Lite Property Characteristics (Cont.)

• SymmetricProperty
• If (x,y) is an instance of the symmetric property
  P, (y,x) is also an instance of P.
• Example friend is a symmetric property. Frank is
  a friend of Deborah ? Deborah is a friend of
  Frank.
• FunctionalProperty
• If a property is a FunctionalProperty, then it
  has no more than one value for each individual
  (it may have no values for an individual).
• The property's minimum cardinality is zero and
  its maximum cardinality is 1.
• Example hasPrimaryEmployer is a
  FunctionalProperty ?no individual may have more
  than one primary employer.

60
OWL Lite Property Characteristics (Cont.)

• InverseFunctionalProperty
• If a property is inverse functional then the
  inverse of the property is functional.
• The inverse of the property has at most one value
  for each individual
• Example, hasUSSocialSecurityNumber is inverse
  functional
• The inverse of this property has at most one
  value for any individual in the class of social
  security numbers.
• No two different individual instances of Person
  have the identical US Social Security Number.
• If two instances of Person have the same social
  security number, then those two instances refer
  to the same individual.

61
OWL Lite Property Restrictions

• allValuesFrom
• Stated on a property with respect to a class.
• This property on this particular class has a
  local range restriction
• Example the class Person may have a property
  called hasDaughter restricted to have
  allValuesFrom the class Woman.
• If an individual person Louise is related by the
  property hasDaughter to the individual Deborah ?
  Deborah is an instance of the class Woman

62
OWL Lite Property Restrictions (Cont.)

• someValuesFrom
• Stated on a property with respect to a class.
• A particular class may have a restriction on a
  property that at least one value for that
  property is of a certain type.
• Example the class SemanticWebPaper may have a
  someValuesFrom restriction on the hasKeyword
  property that states that some value for the
  hasKeyword property should be an instance of the
  class SemanticWebTopic.

63
OWL Lite Restricted Cardinality

• The restrictions constrain the cardinality of
  that property on instances of that class
• OWL Lite cardinality restrictions only allow
  statements concerning cardinalities of value 0 or
  1
• minCardinality
• If a minCardinality of 1 is stated on a property
  w.r.t a class, then any instance of that class
  will be related to at least one individual by
  that property
• Another way of saying that the property is
  required
• Example
• Person's hasOffspring (no minimum cardinality)
• Parent's hasOffspring (minimum cardinality 1)

64
OWL Lite Restricted Cardinality (Cont.)

• maxCardinality
• If a maxCardinality of 1 is stated on a property
  w.r.t a class, then any instance of that class
  will be related to at most one individual by that
  property.
• A maxCardinality 1 restriction is called a
  functional or unique property.
• Example
• The property hasRegisteredVotingState on the
  class UnitedStatesCitizens may have a maximum
  cardinality of one
• Instances of the class UnmarriedPerson should not
  be related to any individuals by the property
  hasSpouse.
• A maximum cardinality of zero on the hasSpouse
  property on the class UnmarriedPerson.

65
OWL Lite Restricted Cardinality (Cont.)

• cardinality
• Provided as a convenience when it is useful to
  state that a property on a class has both
  minCardinality 0 and maxCardinality 0 or both
  minCardinality 1 and maxCardinality 1.
• the class Person has exactly one value for the
  property hasBirthMother.

66
OWL Lite Class Intersection

• intersectionOf
• OWL Lite allows intersections of named classes
  and restrictions
• Example, EmployedPerson is the intersectionOf
  Person and EmployedThings (which could be defined
  as things that have a minimum cardinality of 1 on
  the hasEmployer property)
• Any particular EmployedPerson has at least one
  employer

67
Other OWL Lite Characteristics

• OWL Datatypes
• Uses the RDF mechanisms for data values
• OWL Lite Header Information
• Supports notions of ontology inclusion and
  relationships and attaching information to
  ontologies
• OWL Lite Annotation Properties
• Allows annotations on classes, properties,
  individuals and ontology headers
• OWL Lite Versioning
• RDF already has a small vocabulary for describing
  versioning information. OWL significantly extends
  this vocabulary

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OWL DL and OWL Full

• Both OWL DL and OWL Full use the same vocabulary
  although OWL DL is subject to some restrictions.
• OWL DL requires type separation (a class can not
  also be an individual or property, a property can
  not also be an individual or class).
• OWL DL requires that properties are either
  ObjectProperties or DatatypeProperties

69
OWL DL and OWL Full (Cont.)

• oneOf (enumerated classes)
• Classes can be described by enumeration of the
  individuals that make up the class.
• The members of the class are exactly the set of
  enumerated individuals no more, no less.
• Example daysOfTheWeek Sunday, Monday, Tuesday,
  Wednesday, Thursday, Friday, Saturday.
• The maximum cardinality (7) of any property that
  has daysOfTheWeek as its allValuesFrom
  restriction.
• hasValue (property values)
• A property can be required to have a certain
  individual as a value
• Example dutchCitizens can be characterized as
  those people that have theNetherlands as a value
  of their nationality

70
OWL DL and OWL Full (Cont.)

• disjointWith
• Example Man and Woman
• unionOf, complementOf, intersectionOf
• Example
• Using unionOf, we can state that a class contains
  things that are either USCitizens or
  DutchCitizens.
• Using complementOf, we could state that children
  are not SeniorCitizens.

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OWL DL and OWL Full (Cont.)

• minCardinality, maxCardinality, cardinality
• OWL Full allows cardinality statements for
  arbitrary non-negative integers.
• Example DINKs would restrict the cardinality of
  the property hasIncome to a minimum cardinality
  of two (while the property hasChild would have to
  be restricted to cardinality 0).

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Reasoner

• Inference engine
• OWL rely on inference engines (classifiers) to
  compute a class hierarchy and to determine class
  membership of instances based on the properties
  of classes and instances
• Semantic description specifies each legal
  statements intended meaning
• OWL ? FOL (First order logics)
• Can use FOL inference engines
• FaCT (http//citeseer.ist.psu.edu/horrocks98fact.h
  tml)
• JTP (http//www.ksl.stanford.edu/software/JTP/)

73
Protégé for OWL
74
Protégé for OWL (Reasoner)
75
OWL Case Study Wine Agent
76
Outline

• Overview of the wines ontology
• Querying the KB with a reasoner
• Generating output for the user
• Why use an ontology?

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Wine

• A wine is
• A potable liquid
• Produced by at least one maker of type winery
• Made from at least one type of grape (such grapes
  are restricted to wine grapes elsewhere in the
  ontology)
• Comes from a region that is wine-producing
• Has four properties color, sugar, body, and
  flavor.

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Meal Course

• The concept of a meal course underlies pairing of
  a food with a wine.
• Each course is a consumable thing comprising at
  least one food and at least one drink, the latter
  of which is stipulated to be a wine.
• When the user selects a type of course, or an
  individual food that gets mapped to a type of
  course, the agent will consult that course
  definition for restrictions on the constituent
  food or wine.

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Pasta with Spicy Red Sauce

• Suppose the user has selected fra diavolo
• Pasta with spicy red sauce
• The concept is defined as a type of meal course
  where the food must be a pasta with spicy red
  sauce
• Such courses must be a subclass of those with
  specific restrictions on the properties of their
  wines.
• DRINK-HAS-RED-COLOR-RESTRICTION and the like
  appear elsewhere, specifies the properties of
  candidate wines

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Chateau Lafite Rothschild Pauillac

• One wine that matches the above restrictions is
  Pauillac
• A Pauillac whose maker is Chateau Lafite
  Rothschild.
• Together with other statements in the ontology,
  this allows the reasoner to deduce many
  additional facts that this a Medoc wine from
  Bordeaux, in France, and that it is red

ltPauillac rdfID"ChateauLafiteRothschildPauill
ac"gt lthasMaker rdfresource"ChateauLafiteRo
thschild" /gt lt/Pauillacgt
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Pauillac

• Feature full bodies and strong flavors
• Made entirely from cabernet sauvignon grapes
• Pauillacs are a particular subset of Medocs,
  distinguished by their origin in the Pauillac
  region.
• It is through this additional subclass
  relationship that Pauillac are defined elsewhere
  as red and dry.

82
Reasoner

• Following the above example through the ontology
  reveals a straightforward logical path for
  pairing the Pauillac with the selected course
• Creating the course
• Assert the existence of a new course and drink in
  order to handle the user's query

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Reasoner (Cont.)

• Querying for properties
• Ask for the COLOR, BODY, SUGAR, and FLAVOR of
  WINE9.
• The answers are other concepts from the same
  knowledge base RED, DRY, FULL, and STRONG.
• Querying for wines
• Use logical deduction in order to determine which
  if any wines in ontology satisfy those criteria.
• Here the agent asks for all concepts in the
  knowledge base which are of type wine, and which
  feature COLOR, SUGAR, BODY, and FLAVOR as
  properties which are set to RED, DRY, FULL, and
  STRONG, respectively.

84
Querying for Properties
85
Querying for Wines
86
Reasoner (Cont.)

• Querying for varietals
• For any wine retrieved from the knowledge base,
  the agent can now determine its varietal.
• Because it can process semantic information, the
  agent can use the ontology rather than syntactic
  manipulation, i.e. assuming that the varietal is
  the last word of the name.
• Rather, it searches for a concept of type wine of
  which the Chateau Lafite Rothschild Pauillac is
  an instance.

87
Querying for Varietals
88
Output

• The heading follows a template to place the
  inferred property constraints in an English-like
  sentence.
• The link to wines.com follows a tailored set of
  rules to ensure an adequate number of responses.
• If no wine was found in the KB, the search is for
  the conjunction of the desired properties.
• If any individual wines were identified, the wine
  searches for them by name, and also for their
  varietals.
• In addition to responses concerning individual
  wines, the portal might also present suitable
  varietals. In such case the links represent
  wine.com searches for those varietals, created by
  looking up the appropriate codes used by the
  wine.com search engine, and adding them to the
  URL.

89
Wine Web Service

• Wine Agent accesses information about pricing and
  availability of wines through www.wine.com, via a
  rudimentary on-site search engine.
• www.wine.com used as information providing Web
  Service.
• The inputs and outputs to www.wine.com will be
  annotated in DAML-S 0.9 the DAMLOIL/OWL ontology
  for Web Services.
• Markup will enable automated invocation of the
  www.wine.com Web Service, facilitating
  interoperability, semantic translation of wine
  information, and enabling more sophisticated
  searching and filtering of wine information using
  the richer wine ontology provided by the Wine
  Agent.

90
Why Use Ontologies?

• The functionality provided is not unlike that
  which could be provided by a simple look-up
  table.
• Tabular chart where marks appear at the
  intersections of columns and rows representing
  compatible varieties of food and wine
• Suppose that at least some number of cooperating
  parties were using semantic markup to participate
  in this project
• No need to build an enormous database of foods
  and wines
• The definitions would be distributed

91
Why Use Ontologies? (Cont.)

• Benefits
• A restaurant could mark each food item with
  standardized machine-readable definitions
• A wine retailer could mark its wines with
  standardized machine-readable definitions
• new Pauillac Pauillac distinguished features
• Machine readable
• Varietals search -- a program can interact with
  the wine.com search engine via a well-formed
  language.
• Such communications would not only cover wine
  information specific to the given application
  domain and ontology, but also model the very
  process of querying the inventory

92
References

• W3C Web Ontology
• DAML and DAML Ontologies
• Ontology Development 101 A Guide to Creating
  Your First Ontology