Distracting users as per their knowledge: Combining linked open data and word embeddings to enhance history learning

•Fight information overload by semantics-driven filtering and knowledge generation.•A new way of learning History based on natural language processing and Linked Data.•Customized collecting of texts to train neural networks and check user knowledge.•Find and assess relevant interconnections among semantic entities by word vectors. Organizations that preserve and promote heritage must meet the expectatives of sophisticated visitors who, far from wanting simply to be informed, desire to explore engaging and innovative technology-driven experiences which consider their particular interests and encourage them to discover more. We describe an approach based on quiz games that can be exploited in the deployment of such challenging experiences. The game consists of raising multiple-choice questions about a particular theme which is introduced by a Humanities expert through a brief narrative. Given the input text, a question and its right answer, our strategy provides the expert with a set of wrong alternatives (called distractors). These options are chosen from a (semi)automatically-built tailor-made corpus of documents by considering each player’s level of knowledge on the game theme and exploiting Linked Open Data initiatives and natural language processing. On the one hand, automatic selection of distractors assists the Humanities expert to create games about very diverse topics without needing to be a specialist in all of them. On the other one, distractors are related to the right answer of each question in an appealing and meaningful way, which contributes to arouse the visitors’ curiosity and their possible interest in exploring similar experiences in future visits. The work has been experimentally validated, achieving better results than a previous distractor identification strategy.