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We propose an approach to generate natural language questions from knowledge graphs such as DBpedia and YAGO. We stage this in the setting of a quiz game. Our approach, though, is general enough to be applicable in other settings. Given a topic of interest (e.g., Soccer) and a diculty (e.g., hard), our approach selects a query answer, generates a SPARQL(More)
When running large human computation tasks in the real-world, honeypots play an important role for assessing the overall quality of the work produced. The generation of such honeypots can be a significant burden on the task owner as they require specific characteristics in their design and implementation and continuous maintenance when operating data(More)
We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications , for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem , we propose an end-to-end approach. The approach first selects a named(More)
In this thesis we present a novel approach for generating natural language questions, using factual information from a knowledge graph and automatically assessing their difficulty. Our work elicits a further utilization of the knowledge captured in knowledge graphs that could find applications in research, education and leisure. In general, coming up with(More)
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