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IPY and Semantics

IPY and Semantics. Siri Jodha S. Khalsa Paul Cooper Peter Pulsifer Paul Overduin Eugeny Vyazilov Heather lane. Statement of Problem. Each science domain or community develops its own terminology to describe concepts, resources (objects, data) and relationships

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IPY and Semantics

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  1. IPY and Semantics Siri Jodha S. Khalsa Paul Cooper Peter Pulsifer Paul Overduin Eugeny Vyazilov Heather lane

  2. Statement of Problem • Each science domain or community develops its own terminology to describe concepts, resources (objects, data) and relationships • Data discovery and data sharing depend critically on being able to attach unambiguous meaning to the terms used to describe domain knowledge • Generalized metadata standards such as ISO 19115 lack domain-specific elements

  3. Knowledge Organization Systems • Controlled vocabularies • Glossaries & Dictionaries • Thesauri (limited ability to express relationships between terms) • Gazetteers (place names, sometimes classified and categorized) • Classification Schemes (taxonomies) • Ontologies (can create complex model of reality including rules and axioms)

  4. Ontologies • Expressed in a formal conceptual language (UML, ERD, RDF, OWL,...) where symbols, text and rules of grammar are used to express • classes (conceptualizations of objects) • instances of classes • properties of classes • relationships between classes

  5. Why Create an Ontology? • To transfer domain knowledge to scientists and educators outside the domain • To enable reuse of domain knowledge • Ability integrate existing ontologies • To make domain assumptions explicit • Easier to modify models of reality as domain knowledge evolves • To enable domain-independent services, inductive reasoning and natural-language processing • Allow transmission of knowledge across languages • Translation not always one-to-one

  6. The Big Question • Who is going to do it? • DIS subcommittee of domain experts • How will it be funded? • Corporate underwriting? • Prototype

  7. Process • There is no one correct way to model a domain— there are always viable alternatives. • Ontology development is necessarily an iterative process.

  8. Approaches – Top Down • Begin with survey of existing domain knowledge representations in each IPY discipline • Reuse • Many to choose from • Investigate tools for bringing these knowledge bases into a common system

  9. Approaches – Bottom Up • System for assigning subject metadata (tagging) • High level terms from defined domain specification • Leave discovery and simple semantic relationships to web services such as Google • Mechanism for distilling subject metadata once assigned • Once data released, users should be able to assign new tags • Community review and editing (wiki?)

  10. Outcome • Legacy of IPY will be a dynamic system for cross-domain information discovery and retrieval • Community based • Language neutral

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