Linked data for information extraction challenge 2014 tasks and results
Robert Meusel, Heiko Paulheim · MADOC (University of Mannheim) · 2014
Abstract. For making the web of linked data grow, information extraction meth-ods are a good alternative to manual dataset curation, since there is an abundance of semi-structured and unstructured information which can be harvested that way. At the same time, existing Linked Data sets can be used for training and evalu-ating such information extraction systems. In this paper, we introduce the Linked Data for Information Extraction Challenge 2014. Using the example of person data in Microformats, we show how training and testing data can be curated at large scale. Furthermore, we discuss results achieved in the challenge, as well as open problems and future directions for the challenge.