  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 entity from the knowledge graph as
an answer. It then generates a structured triple-pattern query, which yields
the answer as its sole result. If a multiple-choice question is desired, the
approach selects alternative answer options. Finally, our approach uses a
template-based method to verbalize the structured query and yield a natural
language question. A key challenge is estimating how difficult the generated
question is to human users. To do this, we make use of historical data from the
Jeopardy! quiz show and a semantically annotated Web-scale document collection,
engineer suitable features, and train a logistic regression classifier to
predict question difficulty. Experiments demonstrate the viability of our
overall approach.
