  In the past decade, the DBpedia community has put significant amount of
effort on developing technical infrastructure and methods for efficient
extraction of structured information from Wikipedia. These efforts have been
primarily focused on harvesting, refinement and publishing semi-structured
information found in Wikipedia articles, such as information from infoboxes,
categorization information, images, wikilinks and citations. Nevertheless,
still vast amount of valuable information is contained in the unstructured
Wikipedia article texts. In this paper, we present DBpedia NIF - a large-scale
and multilingual knowledge extraction corpus. The aim of the dataset is
two-fold: to dramatically broaden and deepen the amount of structured
information in DBpedia, and to provide large-scale and multilingual language
resource for development of various NLP and IR task. The dataset provides the
content of all articles for 128 Wikipedia languages. We describe the dataset
creation process and the NLP Interchange Format (NIF) used to model the
content, links and the structure the information of the Wikipedia articles. The
dataset has been further enriched with about 25% more links and selected
partitions published as Linked Data. Finally, we describe the maintenance and
sustainability plans, and selected use cases of the dataset from the TextExt
knowledge extraction challenge.
