Understanding Ontologies

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Understanding Ontologies

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Title: 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

9
Graphical Ontology ExampleHuman Resources
10
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.

14
Simple Ontologies and Their Uses
15
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

16
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

17
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

18
A Portion of the UNSPEC Electronic Commerce
Taxonomy
Live plant and Animal
Live animals
Livestock
Subclass of
Cats
Dogs
19
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.

20
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

21
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

22
Use Cases of Web Ontologies
23
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."

24
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

25
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

26
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

28
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.

29
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)

30
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

31
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.

32
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

33
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)

35
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)

37
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

39
Protégé
40
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

41
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.

43
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

44
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.

47
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

51
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

55
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)

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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.

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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

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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

68
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

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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).

72
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/)

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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.

78
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.

79
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

80
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

83
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.

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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.

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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

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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

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