Knowledge Questions from Knowledge Graphs

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

Learning to Reuse Distractors to support Multiple Choice Question Generation in Education

Multiple choice questions (MCQs) are widely used in digital learning systems, as they allow for automating the assessment process. However, due to the increased digital literacy of students and the advent of social media platforms, MCQ tests are widely shared online, and teachers are continuously challenged to create new questions, which is an expensive and time-consuming task. A particularly sensitive aspect of MCQ creation is to devise relevant distractors, i.e., wrong answers that are not easily identifiable as being wrong. This paper studies how a large existing set of manually created answers and distractors for questions over a variety of domains, subjects, and languages can be leveraged to help teachers in creating new MCQs, by the smart reuse of existing distractors. We built several data-driven models based on context-aware question and distractor representations, and compared them with static feature-based models. The proposed models are evaluated with automated metrics and in a realistic user test with teachers. Both automatic and human evaluations indicate that context-aware models consistently outperform a static feature-based approach. For our best-performing context-aware model, on average 3 distractors out of the 10 shown to teachers were rated as high-quality distractors. We create a performance benchmark, and make it public, to enable comparison between different approaches and to introduce a more standardized evaluation of the task. The benchmark contains a test of 298 educational questions covering multiple subjects & languages and a 77k multilingual pool of distractor vocabulary for future research.Comment: 24 pages and 4 figures Accepted for publication in IEEE Transactions on Learning technologie

PolyAQG Framework: Auto-generating assessment questions

Designing and setting assessment questions for examinations is always a necessary task for educators. In this article, we identify the research gaps in (semi-) automatically generating questions by evaluating all the available approaches developed thus far. We then propose a framework that puts together previous approaches and suggests ways to fill in their gaps. One hundred and thirteen pieces of literature relevant to question generation approaches have been reviewed and compared. For each of the approaches, the uniqueness of the techniques is explained. The PolyAQG Framework is presented with an explanation of how it would contribute to the solution of the problem, by improving the variety of the questions, increasing the total number of possible choices of question selections, as well as providing a better quality of questions. Apart from the framework, another novelty in this work is the innovative way a domain ontology is used to generate a wider variety of questions

PolyAQG Framework: Auto-generating assessment questions

Designing and setting assessment questions for examinations is always a necessary task for educators. In this article, we identify the research gaps in (semi-) automatically generating questions by evaluating all the available approaches developed thus far. We then propose a framework that puts together previous approaches and suggests ways to fill in their gaps. One hundred and thirteen pieces of literature relevant to question generation approaches have been reviewed and compared. For each of the approaches, the uniqueness of the techniques is explained. The PolyAQG Framework is presented with an explanation of how it would contribute to the solution of the problem, by improving the variety of the questions, increasing the total number of possible choices of question selections, as well as providing a better quality of questions. Apart from the framework, another novelty in this work is the innovative way a domain ontology is used to generate a wider variety of questions

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

Evaluating the quality of the ontology-based auto-generated questions

An ontology is a knowledge representation structure which has been used in Virtual Learning Environments (VLEs) to describe educational courses by capturing the concepts and the relationships between them. Several ontology-based question generators used ontologies to auto-generate questions, which aimed to assess students' at different levels in Bloom's taxonomy. However, the evaluation of the questions was confined to measuring the qualitative satisfaction of domain experts and students. None of the question generators tested the questions on students and analysed the quality of the auto-generated questions by examining the question's difficulty, and the question's ability to discriminate between high ability and low ability students. The lack of quantitative analysis resulted in having no evidence on the quality of questions, and how the quality is a�affected by the ontology-based generation strategies, and the level of question in Bloom's taxonomy (determined by the question's stem templates). This paper presents an experiment carried out to address the drawbacks mentioned above by achieving two objectives. First, it assesses the auto-generated questions' difficulty, discrimination, and reliability using two statistical methods: Classical Test Theory (CTT) and Item Response Theory (IRT). Second, it studies the effect of the ontology-based generation strategies and the level of the questions in Bloom's taxonomy on the quality of the questions. This will provide guidance for developers and researchers working in the field of ontology-based question generators, and help building a prediction model using machine learning techniques