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Ontology mapping for the semantic web

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Ontology mapping for the semantic web

Ontology mapping for the semantic web

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Ontology mapping for the semantic web Presentation Transcript

  • 1. Ontology Mapping for the Semantic Web
    Mr. worawithsangkatip
    Student Master of Information Technology (IT)
    Faculty Informatics
    Mahasarakham University
    1
  • 2. Agenda
    Introduction
    Semantic Web
    Ontology
    Ontology Mapping
    Ontology Mapping Research
    Survey Ontology Mapping Techniques
    Conclusion
    Future Work
    2
  • 3. Introduction
    Present Web Technology and Semantic web
    Present Semantic web challenges Research
    Present Related Work Research
    Present Concept Research
    3
  • 4. Semantic Web
    Technology Web present
    Web pages are writing in html tag
    Html describes the syntax not the semantic
    Search engine return keyword and link
    Computer not understand the meaning behind information
    4
  • 5. Semantic Web
    Why Semantic Web ?
    the information that the user wants
    Computer understand the meaning behind information
    the semantic web as a component of Web 3.0
    5
  • 6. Semantic Web
    semantic of social connections
    6
    Source : Radar Networks & Nova Spivack, 2007
  • 7. Semantic Web
    Semantic Web from the defines of Tim BernnersLee
    Semantic Web is a group of methods and technologies to allow machines to understand the meaning
    metadata is an important part the semantic web
    7
  • 8. Semantic Web
    8
    Source :The World Wide Web Consortium (W3C)
    Semantic Web stack
  • 9. Ontology
    ontology is one component of the technology Semantic Web.
    A concept to describe the knowledge.
    Standard for understanding the meaning and scope of information of interest.
    Ontology is useful in the search better. With accuracy.
    9
  • 10. Ontology
    ontology can be used instead of semantic information to describe.
    RDF – (Resource Description Framework)
    OWL – (Web Ontology Language) adds semantic meaning
    10
  • 11. Ontology
    RDF (Resource Description Framework)
    a model for expressing metadata as triples (subject-predicate-object)
    Object
    BIC
    Predicate
    site-owner
    Subject
    www.bicmsu.com
    <? xml version= “1.0” encoding= “UTF-16” ?>
    < rdf:RDF
    xmlns:rdf = “http://www.w3.org/1999/02/22-rdf-syntax-ns#”
    <rdf:Descriptionrdf : about = “http://www.bic-msu.com”>
    <mydomain : site-owner>
    BIC (business intelligent center)
    </mydomain : site-owner>
    </rdf:Description>
    </rdf:RDF>
    11
  • 12. Ontology
    OWL (Web Ontology Language)
    OWL is a language that describes the data than the language RDF.
    the class (class), property (properties) and relationships. (Relationship).
    12
    Class1
    Properties:
    properties1: type1
    properties2: type2
    ……
    <owl :ObjectPropertyrdf:ID= “teaches”>
    <rdfs:rangerdf:resource= #course”/>
    <rdfs:domainrdf:resource= #academicStaffMember”/>
    <rdfs:subPropertyOfrdf:resource= #Involves”/>
    </owl: ObjectProperty >
    Class3
    Class2
    Class Hierachical
  • 13. Ontology Mapping
    Why Ontology Mapping ?
    organization has its own ontology.
    use the shared ontology.
    problem in terms of data Replace or concept different.
    13
  • 14. Ontology Mapping
    What is Ontology Mapping Problem ?
    Specification of a conceptualization different
    specification of the terms in the domain and relations different
    14
  • 15. Ontology Mapping
    Type of Ontology Mapping
    15
    Ontology Mapping
    Ontology Alignment
    Ontology Merging
  • 16. Ontology mapping
    Ontology mapping is important
    database integration,heterogeneous database retrieval (traditional)
    catalog matching (e-commerce)
    agent communication (theory only)
    web service integration (urgent)
    P2P information sharing (emerging)
    personalisation (emerging)
    16
  • 17. Ontology Mapping Research
    Semantic web Challenges [1]
    17
    A Classification of the Semantic Web Challenges
  • 18. Ontology Mapping Research
    Research Classification of Ontology Mapping [2]
    Mapping Discovery
    Declarative formal representation of mappings
    Reasoning with mappings
    18
  • 19. Survey Ontology Mapping Techniques
    GLUE
    PROMPT
    QOM
    19
  • 20. GLUE [3] Machine learning techniques to find mappings.
    GLUE architecture consists of
    Distribution Estimator
    Similarity Estimator.
    Relaxation Labeler
    GLUE output's one to one correspondences between the taxonomies the ontologies .
    String similarity, structure and machine learning strategies.
    20
    Survey Ontology Mapping Techniques
  • 21. PROMPT [4]
    Input: Two ontology's in OWL/ OKBC
    Output: Suggestions of mapping and
    a merging ontology based on the choice made by the user.
    iPROMPT : Interactive ontology merging tool.
    AnchorPROMT : Graph-based mappings to
    provide additional information for iPROMPT.
    PROMPTDiff : Compares different ontology
    versions by combining matchers in a fixed point manner.
    PROMPTFactor : Tool for extracting a part of an ontology.
    21
    Survey Ontology Mapping Techniques
  • 22. QOM [5]
    String similarity, structure and instances.
    Input : Two OWL or RDFS ontology's with elements (e.g., classes, properties, instances) in the ontology's
    Output: One-to-one or one-to-none correspondences.
    22
    Survey Ontology Mapping Techniques
  • 23. Conclusion
    Semantic web in a range of research and development.
    Semantic web research is innovative and important.
    Now in the era of web 3.0.
    23
  • 24. Future Work
    Research Ontology Mapping New Techniques
    Publish Research of survey ontology mapping techniques
    24
  • 25. References
    [1] Keyvanpour M, Hassanzadeh H, Mohammadizadeh B. "Comparative Classification of Semantic Web Challenges and Data Mining Techniques". Web Information Systems and Mining, 2009. Shanghai 2009. 200-203.
    [2] - N. Noy. Semantic Integration: A Survey of Ontology-based Approaches. SigmondRecord, Special Issue on Semantic Integration. 2004
    25
  • 26. Questions?
    Thank You.
    26