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In the past couple of years, there has been a significant increase of the amount of false information on the web. The falsehoods quickly spread through social networks reaching a wider audience than ever before. This poses new challenges to our society as we have to reevaluate which information source we should trust and how we consume and distribute content on the web. As a response to the rising amount of disinformation on the Internet, the number of fact-checking platforms has increased. On these platforms, professional fact-checkers validate the published information and make their conclusions publicly available. Nevertheless, the manual validation of information by fact-checkers is laborious and time-consuming, and as a result, not all of the published content can be validated. Since the conclusions of the validations are released with a delay, the interest in the topic has often already declined, and thus, only a small fraction of the original news consumers can be reached. Automated fact-checking holds the promise to address these drawbacks as it would allow fact-checkers to identify and eliminate false information as it appears on the web and before it reaches a wide audience. However, despite significant progress in the field of automated fact-checking, substantial challenges remain: (i) The datasets available for training machine learning-based fact-checking systems do not provide high-quality annotation of real fact-checking instances for all the tasks in the fact-checking process. (ii) Many of today’s fact-checking systems are based on knowledge bases that have low coverage. Moreover, because for these systems sentences in …