NERD meets NIF: Lifting NLP extraction results to the Linked Data Cloud

leipzig.de We have often heard that data is the new oil. In particular, extracting information from semi-structured textual docu-ments 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, classi-fying them according to pre-defined taxonomies and disam-biguating them through URIs identifying real world entities. As a step towards interconnecting the Web of documents via those entities, different extractors have been proposed. Al-though they share the same main purpose (extracting named entity), they differ from numerous aspects such as their un-derlying dictionary or ability to disambiguate entities. We have developed NERD, an API and a front-end user inter-face powered by an ontology to unify various named entity extractors. The unified result output is serialized in RDF according to the NIF specification and published back on the Linked Data cloud. We evaluated NERD with a dataset composed of five 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