# Question Generation from a Knowledge Base with Web Exploration

arXiv: https://arxiv.org/abs/1610.03807 · HTML: https://mlsanity.com/paper/1610.03807/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2016.

## Built on by (most cited first, 5 of 5)

- [Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs](https://mlsanity.com/paper/2205.00355/) (2022)
- [AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL](https://mlsanity.com/paper/2208.12461/) (2022)
- [Automatic question generation based on sentence structure analysis using machine learning approach](https://mlsanity.com/paper/2205.12811/) (2022)
- [Qsnail: A Questionnaire Dataset for Sequential Question Generation](https://mlsanity.com/paper/2402.14272/) (2024)
- [Beyond the Textual: Generating Coherent Visual Options for MCQs](https://mlsanity.com/paper/2508.18772/) (2025)

## Abstract (arXiv)

Question generation from a knowledge base (KB) is the task of generating questions related to the domain of the input KB. We propose a system for generating fluent and natural questions from a KB, which significantly reduces the human effort by leveraging massive web resources. In more detail, a seed question set is first generated by applying a small number of hand-crafted templates on the input KB, then more questions are retrieved by iteratively forming already obtained questions as search queries into a standard search engine, before finally questions are selected by estimating their fluency and domain relevance. Evaluated by human graders on 500 random-selected triples from Freebase, questions generated by our system are judged to be more fluent than those of newciteserban-EtAl:2016:P16-1 by human graders.