R
René Speck
Researcher at Leipzig University
Publications - 24
Citations - 514
René Speck is an academic researcher from Leipzig University. The author has contributed to research in topics: Semantic Web & Knowledge extraction. The author has an hindex of 8, co-authored 24 publications receiving 468 citations. Previous affiliations of René Speck include University of Paderborn.
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Proceedings ArticleDOI
GERBIL: General Entity Annotator Benchmarking Framework
Ricardo Usbeck,Michael Röder,Axel-Cyrille Ngonga Ngomo,Ciro Baron,Andreas Both,Martin Brümmer,Diego Ceccarelli,Marco Cornolti,Didier Cherix,Bernd Eickmann,Paolo Ferragina,Christiane Lemke,Andrea Moro,Roberto Navigli,Francesco Piccinno,Giuseppe Rizzo,Harald Sack,René Speck,Raphaël Troncy,Jörg Waitelonis,Lars Wesemann +20 more
TL;DR: GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.
Book ChapterDOI
Ensemble Learning for Named Entity Recognition
TL;DR: This work combines four different state-of-the approaches by using 15 different algorithms for ensemble learning and evaluates their performace on five different datasets to suggest that ensemble learning can reduce the error rate of state- of-the-art named entity recognition systems by 40%, thereby leading to over 95% f-score in the best run.
Journal ArticleDOI
DeFacto-Temporal and multilingual Deep Fact Validation
Daniel Gerber,Diego Esteves,Jens Lehmann,Lorenz Bühmann,Ricardo Usbeck,Axel-Cyrille Ngonga Ngomo,René Speck +6 more
TL;DR: DeFacto (Deep Fact Validation)-an algorithm able to validate facts by finding trustworthy sources for them on the Web by supplying the user with relevant excerpts of web pages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact.
Book ChapterDOI
SCMS: semantifying content management systems
TL;DR: The SCMS (Semantic Content Management Systems) framework is described, whose main goals are the extraction of knowledge from unstructured data in any CMS and the integration of the extracted knowledge into the same CMS.
Proceedings Article
Named entity recognition using FOX
TL;DR: This framework achieves a higher F-measure than state-of-the-art named entity recognition frameworks by combining the results of several approaches through ensemble learning, and disambiguates and links named entities against DBpedia by relying on the AGDISTIS framework.