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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 and representations – Thesauri, Dictionary, Taxonomies, DB Schema – XML Schema, DTDs, UML, RDF Schema – Might contain is_a, classes, partof, operations, Behaviour axioms, synonyms, hyponym Representational Constructs • Classes, attributes • Relationships – Is-a, part-of, non-standard • • • • Events with Spatio-temporal characteristics Uncertainties ? Visual/Iconic constructs Multiple Languages 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 Next Step • Challenges for the Database Community – Storing, retrieval, querying – Browsing, interoperation • Of heterogeneous Ontologies 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) 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 Ontology Search Compare/Similarity Requirements/ Analysis Ontology Learning Merge/ Refine/Assemble Evaluation Maintenance Versioning Creation/ Change Consistency Checking Deployment (e.g., Hypothesis Generation, Query) 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 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) DB Research in the Ontology LifeCycle III • Ontology Versioning – Collaborative editing – Meta Model specific versioning – Version of Schema/Meta Model Transformations 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)