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Bringing the IPTC News Architecture into the Semantic Web

  • Raphaël Troncy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5318)

Abstract

For easing the exchange of news, the International Press Telecommunication Council (IPTC) has developed the NewsML Architecture (NAR), an XML-based model that is specialized into a number of languages such as NewsML G2 and EventsML G2. As part of this architecture, specific controlled vocabularies, such as the IPTC News Codes, are used to categorize news items together with other industry-standard thesauri. While news is still mainly in the form of text-based stories, these are often illustrated with graphics, images and videos. Media-specific metadata formats, such as EXIF, DIG35 and XMP, are used to describe the media. The use of different metadata formats in a single production process leads to interoperability problems within the news production chain itself. It also excludes linking to existing web knowledge resources and impedes the construction of uniform end-user interfaces for searching and browsing news content.

In order to allow these different metadata standards to interoperate within a single information environment, we design an OWL ontology for the IPTC News Architecture, linked with other multimedia metadata standards. We convert the IPTC NewsCodes into a SKOS thesaurus and we demonstrate how the news metadata can then be enriched using natural language processing and multimedia analysis and integrated with existing knowledge already formalized on the Semantic Web. We discuss the method we used for developing the ontology and give rationale for our design decisions. We provide guidelines for re-engineering schemas into ontologies and formalize their implicit semantics. In order to demonstrate the appropriateness of our ontology infrastructure, we present an exploratory environment for searching and browsing news items.

Keywords

Football Player News Story News Item SPARQL Query Semantic Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Raphaël Troncy
    • 1
  1. 1.CWI AmsterdamAmsterdamThe Netherlands

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