MQALD

Question Answering (QA) over Knowledge Graphs (KG) has the aim of developing a system that is capable of answering users' questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata and so on.<br> This kind of system needs to translate the question of the user, written using natural language, into a query formulated through a data query language that is compliant with the underlying KG.<br> The translation process is already non-trivial to solve even when trying to answer simple questions that involve a single triple pattern but becomes troublesome when trying to cope with questions that require the presence of modifiers in the final query, i.e. aggregate functions, query forms, and so on.<br> The attention over this aspect is growing but has never been thoroughly addressed by the existing literature.<br> Starting from the latest advances in this field, we want to make a further step towards this direction by giving a comprehensive description of this topic and the main issues revolving around it and making publicly available a dataset designed to evaluate the performance of a QA system in translating such articulated questions into a specific data query language. <br> This dataset has also been used to evaluate the best QA systems available at the state of the art.