Inference on the Semantic Web

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This slide includes inference technology on the Semantic Web

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  • 1. Inferenceon the Semantic Web
    Myungjin Lee
  • 2. Artificial Intelligence
  • 3. Fussy
    System
    the intelligence of machines
    methodology
    Machine
    Learning
    Neural
    Network
    goal
    methodology
    hasApproach
    Artificial
    Intelligence
    Genetic
    Algorithm
    methodology
    Knowledge
    Base
    Approach
    Logic
    hasApproach
    basedon
    Approaches of AI
  • 4. What is Semantic Web?
    Web
    target
    Semantic
    Web
    Artificial
    Intelligence
    goal
    the intelligence of machines
    purpose
    a vision of information
    that is understandable by computers,
    so that they can perform
    more of the tedious work involved
    in finding, sharing, and combining information
    on the web.
    dc:description
  • 5. approach
    Approach of Semantic Web
    Semantic
    Web
    Knowledge
    Base
    Approach
    Logic
    Sentence
    basedon
    basedon
    use
    representation
    representation
    Ontology
    representation
    Propositional
    Logic
    Predicate
    Logic
    Fist Order
    Logic
    Description
    Logic
    partOf
    partOf
  • 6. Ontology on the Semantic Web
    OWL
    Ontology
    component
    SCOT
    component
    RDF
    RDFS
    vocabulary
    component
    SKOS
    dc:description
    XML
    component
    vocabulary
    SIOC
    URI
    An ontology is a formal
    explicit specification of
    a conceptualization.
    FOAF
  • 7. Merits of Ontology
    Database
    Ontology
    owl:sameAs
    ¬
    owl:differentFrom
    image
    image
    differences
    differences
    rdf:Bag
    rdf:li
    rdf:li
    rdf:li
    a power
    of
    represen-tation
    Inference
    Semantics
  • 8. Task of Inference
    Inference
    being able to derive new data
    from data that you already know
    dc:description
    task
    task
    dc:description
    Rule
    Inference
    TBox
    Inference
    Ontology
    Inference
    statements
    that describe a system
    in terms of controlled
    vocabularies
    task
    dc:description
    task
    dc:description
    ABox
    Inference
    TBox-compliant statements
    about that vocabulary
    to produce valid statements
    within system
    based on rule
  • 9. Ontology Inference
    Ontology
    Inference
    to derive additional facts to be inferred
    from instance data and class descriptions
    based on own semantics
    dc:description
    RDF Semantics
    Person
    <x, y> is in IEXT(I(rdfs:subClassOf))
    if and only if x and y are in IC
    and ICEXT(x) is a subset of ICEXT(y)
    Man
    Myungjin
    ( Man rdfs:subClassOf Person )
    ( Myungjinrdf:type Man )
    ( Myungjinrdf:type Person )
  • 10. TBox Inference
    TBox
    Inference
    Ontology
    Inference
    statements
    that describe a system
    in terms of controlled
    vocabularies
    dc:description
    task
    <rdfs:Classrdf:about="http://xmlns.com/foaf/0.1/Document" rdfs:label="Document”>
    <rdfs:subClassOfrdf:resource="http://xmlns.com/wordnet/1.6/Document"/>
    </rdfs:Class>
    <rdfs:Classrdf:about="http://xmlns.com/foaf/0.1/PersonalProfileDocument”>
    <rdfs:subClassOfrdf:resource="http://xmlns.com/foaf/0.1/Document"/>
    </rdfs:Class>
    http://xmlns.com/foaf/0.1/PersonalProfileDocument
    rdfs:subClassOf
    http://xmlns.com/wordnet/1.6/Document
  • 11. ABox Inference
    Ontology
    Inference
    ABox
    Inference
    TBox-compliant statements
    about that vocabulary
    dc:description
    task
    <rdf:Propertyrdf:about="http://xmlns.com/foaf/0.1/homepage” rdfs:label="homepage“ >
    <rdfs:subPropertyOfrdf:resource="http://xmlns.com/foaf/0.1/page"/>
    </rdf:Property>
    <foaf:Personrdf:about="#me" xmlns:foaf="http://xmlns.com/foaf/0.1/">
    <foaf:name>Dan Brickley</foaf:name>
    <foaf:homepagerdf:resource="http://danbri.org/" />
    </foaf:Person>
    http://xmlns.com/foaf/0.1 /#me foaf:page http://danbri.org/
  • 12. Rule Inference
    Rule
    Inference
    to produce valid statements
    within system
    based on rule
    dc:description
    if hasParent(?x, ?y)
    hasParent(?x, ?z)
    Man(?y)
    Woman(?z)
    then hasWife(?y, ?z)
    hasWife
    hasParent
    hasParent
  • 13. SWRL (Semantic Web Rule Language)
    SWRL
    Horn-like
    Rule
    Member
    Submission
    representation
    status
    editor
    form
    subLanguage
    SWRLTab
    RuleML
    Body
    rdf:Seq
    rdf:li
    rdf:li
    Head
    plugIn
    Protégé
    screenshot
  • 14. Inference Engine for Semantic Web
    Bossam
    a forward chaining rule engine supports SWRL
    dc:description
    rdf:type
    Pellet
    an open-source Java OWL DL reasoner has SWRL-support
    dc:description
    Inference
    Engine
    rdf:type
    KAON2
    an infrastructure for managing OWL-DL, SWRL, and F-Logic ontologies
    dc:description
    rdf:type
    Racer
    Pro
    rdf:type
    processing of rules in a SWRL-based syntax by translating them into nRQL rules
    dc:description
    rdf:type
    Jena
    to derive additional RDF assertions, the axioms and rules associated with the reasoner
    dc:description
  • 15. SMART System
    Intelligence
    Information
    System
    Lab
    Yonsei
    University
    java framework for semantic web application
    locatedIn
    created
    dc:description
    SMART
    function
    support
    rdf:Bag
    rdf:Bag
    rdf:li
    rdf:li
    rdf:li
    rdf:li
    rdf:li
    Ontology
    Process
    SPARQL
    Process
    rdf:li
    SPARQL
    RDF
    RDFS
    Rule
    Inference
    Ontology
    Inference
    SWRL
    OWL
  • 16. Example Demo
    SWRL Rule
    if sioc:Post(?x)
    sioc:Post(?y)
    sioc:topic(?x, ?a)
    sioc:topic(?y, ?b)
    rdf:type(?a, ?z)
    rdf:type(?b, ?z)
    then sioc:related_to(?x, ?y)
    sioc:Post
    sioc:Post
    rdf:type
    rdf:type
    sioc:related_to
    clouds-with-sioc
    sample-post
    sioc:topic
    sioc:topic
    semantic-web
    sws.geonames.org
    SPARQL Query
    PREFIX sioc: <http://rdfs.org/sioc/ns#>
    SELECT ?u ?v
    WHERE
    {
    ?u sioc:related_to ?v .
    }
    rdf:type
    rdf:type
    semanticweb
  • 17. Issue of Inference
    RDF 상에서 어디에 추론을 쓰지?
    새로운 관계 발견을 통한 네트워크 분석
    또 다른 RDF Vocabularies 혹은 도메인 온톨로지와의 관계 규칙 정의 및 추론
    고민할 문제
    추론을 위한 표현력과 복잡도
    많은 룰에 의한 충돌
    세상사를 반영한 규칙의 생성
    철저한 준비?
    표현력의 한계?
  • 18. ? !