# Knowledge Questions from Knowledge Graphs

arXiv: https://arxiv.org/abs/1610.09935 · HTML: https://mlsanity.com/paper/1610.09935/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

## Built on by (most cited first, 15 of 15)

- [Quiz-Style Question Generation for News Stories](https://mlsanity.com/paper/2102.09094/) (2021)
- [InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration](https://mlsanity.com/paper/2402.11441/) (2024)
- [SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts](https://mlsanity.com/paper/2412.00765/) (2024)
- [Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model](https://mlsanity.com/paper/2110.06560/) (2021)
- [AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL](https://mlsanity.com/paper/2208.12461/) (2022)
- [GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation](https://mlsanity.com/paper/2505.20416/) (2025)
- [Learning to Reuse Distractors to support Multiple Choice Question Generation in Education](https://mlsanity.com/paper/2210.13964/) (2022)
- [Systematic Assessment of Factual Knowledge in Large Language Models](https://mlsanity.com/paper/2310.11638/) (2023)
- [How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering](https://mlsanity.com/paper/2401.05777/) (2024)
- [KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs](https://mlsanity.com/paper/2406.10802/) (2024)
- [UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization](https://mlsanity.com/paper/2306.06851/) (2023)
- [Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity](https://mlsanity.com/paper/2408.12850/) (2024)
- [Compositionality-Aware Graph2Seq Learning](https://mlsanity.com/paper/2201.12178/) (2022)
- [Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2604.10748/) (2026)
- [ChronoQG: Towards a Temporally Expressive and Hop-Bounded Benchmark for Temporal Knowledge Graph Question Generation](https://mlsanity.com/paper/2607.14770/) (2026)

## Abstract (arXiv)

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.