title: Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure.
abstract: In this paper, we present a semi-automatic system (Sherlock) for quiz generation using linked data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated quizzes. Difficulty scaling is non-trivial, and it is fundamentally related to cognitive science. We approach the problem with a new angle by perceiving the level of knowledge difficulty as a similarity measure problem and propose a novel hybrid semantic similarity measure using linked data. Extensive experiments show that the proposed semantic similarity measure outperforms four strong baselines with more than 47 % gain in clustering accuracy. In addition, we discovered in the human quiz test that the model accuracy indeed shows a strong correlation with the pairwise quiz similarity.
authors.affiliation: Department of Computing Science, University of Aberdeen, Aberdeen, AB24 3UE UK.
authors.forename: Chenghua
authors.initials: C
authors.lastname: Lin
authors.affiliation: BBC Future Media and Technology - Knowledge and Learning, BBC Bridge House, MediaCityUK, Salford, M50 2QH UK.
authors.forename: Dong
authors.initials: D
authors.lastname: Liu
authors.affiliation: Department of Computing Science, University of Aberdeen, Aberdeen, AB24 3UE UK.
authors.forename: Wei
authors.initials: W
authors.lastname: Pang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, 130012 China.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Wang
keywords: Educational games; Linked data; Quiz generation; RDF; Semantic similarity; Text analytics