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Knowledge Questions from Knowledge Graphs

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45767

Yahya,  Mohamed
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44119

Berberich,  Klaus
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Volltexte (frei zugänglich)

arXiv:1610.09935.pdf
(Preprint), 593KB

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Zitation

Seyler, D., Yahya, M., & Berberich, K. (2016). Knowledge Questions from Knowledge Graphs. Retrieved from http://arxiv.org/abs/1610.09935.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002C-1CB5-F
Zusammenfassung
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.