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Semantic Integration

Semantic Integration






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    Semantic Integration Semantic Integration Presentation Transcript

    • Semantic Integration: A Survey Of Ontology-Based Approaches
    • Reference
      • Natalya F. Noy "Semantic Integration: A Survey Of Ontology-Based Approaches," SIGMODRecord , Vol. 33, No. 4, December 2004.
    • Abstract
      • Semantic integration is an active area of research in several disciplines, such as
        • databases
        • information-integration
        • Ontologies
    • Ontologies and semantic integration
      • Ontologyis some formal description of
        • A domain of discourse
        • Intended for sharing among different applications
        • Expressed in a language that can be used for reasoning
      • Create artifacts that different applications can share
    • Ontologies and semantic integration (cont’)
      • The goals of using ontologies is not to have the problem of heterogeneity
      • Knowledge sharing
      • Mapping between ontologies
    • The types of differences between ontologies
      • Language level mismatches
        • The languages can differ in their syntax
      • The normalization process
        • Precedes ontology-matching
        • Translates source ontologies to the same language
    • The types of differences between ontologies (cont’)
      • Ontology level mismatches
        • Using the same linguistic terms to describe different concepts
        • Using different modeling paradigms
        • Using different modeling conventions and levels of granularity
    • Three dimensions of semantic integration research
      • Mapping discovery
      • Declarative formal representations of mappings
      • Reasoning with mappings
    • Discovering mappings
      • The extensions of ontology
        • Finding correspondences between two extensions
      • Use various characteristics of ontologies
        • Comprises heuristics-based or machine learning techniques
    • Using a shared ontology
      • A standard upper ontology that will promote
        • data interoperability
        • Information search and retrieval
        • Automated inferencing
        • Natural language processing
    • Using a shared ontology (cont’)
      • Reuse the foundational ontologies
        • Facilitate semantic interoperation between applications
      • Underlying natural language and human common-sense
    • Ontology alignment
      • Concept names and natural-language descriptions
      • Class hierarchy (subclass–superclass relationships)
      • Instances of classes
      • Class descriptions (as in DL-based tools)
    • Ontology alignment (cont’)
      • Property definitions (domains, ranges, restrictions)
      • Class descriptions (as in DL-based tools)
    • Representations of mappings
      • Representing mappings as instances in an ontology of mappings
      • Defining bridging axioms in logic to represent transformations
      • Using views to describe mappings from a global ontology to local ontologies
    • Ontology translation
      • Given the mapping between the source and target
      • the mapping and performs inference on the merged ontology
      • Performs a projection step
    • Conclusion
      • Share and reuse the techniques that they have developed in their respective domains
      • Semantic web issue
      • Machine-interpretable ontologies