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Ontology Working Group Wrap Up

Ontology Working Group Wrap Up. 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

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Ontology Working Group Wrap Up

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  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 (> 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 (> 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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