H
Heiko Paulheim
Researcher at University of Mannheim
Publications - 267
Citations - 6909
Heiko Paulheim is an academic researcher from University of Mannheim. The author has contributed to research in topics: Linked data & Computer science. The author has an hindex of 35, co-authored 239 publications receiving 5629 citations. Previous affiliations of Heiko Paulheim include Zentrum für Europäische Wirtschaftsforschung & Technische Universität Darmstadt.
Papers
More filters
Proceedings ArticleDOI
Weakly supervised learning for fake news detection on Twitter
TL;DR: A weakly supervised approach, which automatically collects a large-scale, but very noisy training dataset comprising hundreds of thousands of tweets, and shows that despite this unclean inaccurate dataset, it is possible to detect fake news with an F1 score of up to 0.9.
Proceedings Article
A large database of hypernymy relations extracted from the Web
Julian Seitner,Christian Bizer,Kai Eckert,Stefano Faralli,Robert Meusel,Heiko Paulheim,Simone Paolo Ponzetto +6 more
TL;DR: A publicly available database containing more than 400 million hypernymy relations extracted from the CommonCrawl web corpus is presented, which represents a rich source of knowledge and may be useful for many researchers.
Book ChapterDOI
A collection of benchmark datasets for systematic evaluations of machine learning on the Semantic Web
TL;DR: A collection of 22 benchmark datasets of different sizes can be used to conduct quantitative performance testing and systematic comparisons of approaches for machine learning on the Semantic Web.
Journal ArticleDOI
The Mannheim Search Join Engine
TL;DR: The Mannheim Search Join Engine is presented which automatically performs table extension operations based on a large corpus of Web data originating from the Web or corporate intranets and achieves a coverage close to 100% and a precision around 90% for the tasks of extending tables describing cities, companies, countries, drugs, books, films, and songs.
Book ChapterDOI
Detecting Incorrect Numerical Data in DBpedia
Dominik Wienand,Heiko Paulheim +1 more
TL;DR: The application of unsupervised numerical outlier detection methods to DBpedia, using Interquantile Range (IQR), Kernel Density Estimation (KDE), and various dispersion estimators, combined with different semantic grouping methods are studied.