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Erschienen in:
2014 | OriginalPaper | Buchkapitel
TRTML - A Tripleset Recommendation Tool Based on Supervised Learning Algorithms
verfasst von : Alexander Arturo Mera Caraballo, Narciso Moura Arruda Jr., Bernardo Pereira Nunes, Giseli Rabello Lopes, Marco Antonio Casanova
Erschienen in: The Semantic Web: ESWC 2014 Satellite Events
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Abstract
The Linked Data initiative promotes the publication of interlinked RDF triplesets, thereby creating a global scale data space. However, to enable the creation of such data space, the publisher of a tripleset \(t\) must be aware of other triplesets that he can interlink with \(t\). Towards this end, this paper describes a Web-based application, called TRTML, that explores metadata available in Linked Data catalogs to provide data publishers with recommendations of related triplesets. TRTML combines supervised learning algorithms and link prediction measures to provide recommendations. The evaluation of the tool adopted as ground truth a set of links obtained from metadata stored in the DataHub catalog. The high precision and recall results demonstrate the usefulness of TRTML.