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Ontology Working Group Wrap Up

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Ontology Working Group Wrap Up What are Ontologies? Conceptualizations of real world Often derived in Consensus processes or enforced by entities Variety of content ... – PowerPoint PPT presentation

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Title: Ontology Working Group Wrap Up


1
Ontology Working Group Wrap Up
2
What are Ontologies?
  • Conceptualizations of real world
  • Often derived in Consensus processes or enforced
    by entities
  • Variety of content and representations
  • Thesauri, Dictionary, Taxonomies, DB Schema
  • XML Schema, DTDs, UML, RDF Schema
  • Might contain is_a, classes, partof, operations,
    Behaviour axioms, synonyms, hyponym

3
Representational Constructs
  • Classes, attributes
  • Relationships
  • Is-a, part-of, non-standard
  • Events with Spatio-temporal characteristics
  • Uncertainties ?
  • Visual/Iconic constructs
  • Multiple Languages

4
Examples of Applications
  • Standards
  • UMLS Metathesaurus
  • Yahoo, Open Directory
  • Business Process Modeling Initiative (BPMI)
  • XML-HR Initiative (Human Resouces)
  • PapiNet (Paper Industry)
  • Application
  • Genom Research Exchange
  • B2B Exchange (product catalog interoperation,
    business process interoperation)
  • Mediation across multi-lingual ontologies

5
Next Step
  • Challenges for the Database Community
  • Storing, retrieval, querying
  • Browsing, interoperation
  • Of heterogeneous Ontologies

6
Database Issues
  • Support for Ontologies
  • Acquiring Ontologies
  • Machine Learning
  • Learning from User Practices
  • Reusing existing Ontologies
  • Ontology Merging (resolution of
    differences/mismatch in representing same or
    similar things)

7
Database Issues for Ontology Management
  • Support technology depends on the tasks to
    perform
  • Comprehensive Data Management support requires
    the identification of the ontology life cycle

8
Ontology Search
Compare/Similarity
Merge/Refine/Assemble
Requirements/Analysis
Evaluation
MaintenanceVersioning
OntologyLearning
Creation/ Change
ConsistencyChecking
Deployment (e.g., Hypothesis Generation, Query)
9
DB Research in the Ontology LifeCycle
  • Operations to compare Models/Ontologies
  • Scalability/Storage Indexing of Ontologies
  • DB approaches data model specific
  • Need to support graph based data models
  • Temporal Query Languages

10
DB Research in the Ontology LifeCycle II
  • Schema Mapping
  • Meta Model specific
  • Representation of exceptions, e.g., tweety
  • Specification of Inexact Schema Correspondences
  • E.g., 40 of animals are 30 of humans
  • Meta Model Transformations/Mappings (e.g., UML to
    RDF Schema)

11
DB Research in the Ontology LifeCycle III
  • Ontology Versioning
  • Collaborative editing
  • Meta Model specific versioning
  • Version of Schema/Meta Model Transformations

12
DB Research Semantic Interoperation
  • Inference v/s Query Rewriting/Processing for
    Semantic Integration
  • E.g., RichPerson (AND Person (gt Salary 100))
  • Can Query Processing/Concept Rewriting provide
    the same functionality as inferences ? More
    efficiently ?
  • Distributed Inferences and Loss of Information
  • Query Languages for combining metadata and data
    queries
  • Graph-based data models and query languages
  • Schema Correspondences/Mappings (Repeat from
    previous slide)
  • Intensional Answers (Answers are descriptions,
    e.g. (AND Person (gt Salary 100)) instead of a
    list of all rich people)
  • Semantic Associations (identification of
    meaningful relationships between different types
    of instances)
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