NERD meets NIF: Lifting NLP Extraction Results to the Linked Data Cloud

NERD meets NIF: Lifting NLP Extraction Results to the Linked Data CloudDownload PDF

Author: Giuseppe Rizzo, Raphaël Troncy, Sebastian Hellmann and Martin Bruemmer

In Proceedings of The 5th Workshop on Linked Data on the Web (LDOW’12) held in Lyon, France on April 16, 2012. The workshop is co-located with the World Wide Web 2012 conference (WWW2012).

We have often heard that data is the new oil. In particular, extracting information from semi-structured textual documents on the Web is key to realize the Linked Data vision. Several attempts have been proposed to extract knowledge from textual documents, extracting named entities, classifying them according to prede ned taxonomies and disambiguating them through URIs identifying real world entities. As a step towards interconnecting the Web of documents via those entities, di erent extractors have been proposed. Although they share the same main purpose (extracting named entity), they di er from numerous aspects such as their underlying dictionary or ability to disambiguate entities. We have developed NERD, an API and a front-end user interface powered by an ontology to unify various named entity extractors. The uni ed result output is serialized in RDF according to the NIF speci cation and published back on the Linked Data cloud. We evaluated NERD with a dataset composed of ve TED talk transcripts, a dataset composed of 1000 New York Times articles and a dataset composed of the 217 abstracts of the papers published at WWW 2011.

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