SCARLET: SemantiC RelAtion DiscoveRy by Harvesting OnLinE OnTologies

Reka Marta Sabou; Mathieu d’Aquin; Enrico Motta

doi:10.1007/978-3-540-68234-9_72

SCARLET: SemantiC RelAtion DiscoveRy by Harvesting OnLinE OnTologies

Reka Marta Sabou, Mathieu d’Aquin, Enrico Motta

Data, Process and Knowledge Management (Sabou)

Publikation: Konferenzbeitrag › Konferenzposter

Abstract

We present a demo of SCARLET, a technique for discover-
ing relations between two concepts by harvesting the Semantic Web, i.e.,
automatically finding and exploring multiple and heterogeneous online
ontologies. While we have primarily used SCARLET’s relation discovery
functionality to support ontology matching and enrichment tasks, it is
also available as a stand alone component that can potentially be inte-
grated in a wide range of applications. This demo will focus on presenting
SCARLET’s functionality and its different parametric settings that can
influence the trade-off between its accuracy and time performance.

Originalsprache	Englisch
DOIs	https://doi.org/10.1007/978-3-540-68234-9_72
Publikationsstatus	Veröffentlicht - 2008

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

102
102001 Artificial Intelligence
102015 Informationssysteme
102022 Softwareentwicklung

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Zitat

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AB - We present a demo of SCARLET, a technique for discover- ing relations between two concepts by harvesting the Semantic Web, i.e., automatically finding and exploring multiple and heterogeneous online ontologies. While we have primarily used SCARLET’s relation discovery functionality to support ontology matching and enrichment tasks, it is also available as a stand alone component that can potentially be inte- grated in a wide range of applications. This demo will focus on presenting SCARLET’s functionality and its different parametric settings that can influence the trade-off between its accuracy and time performance.

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