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.
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.
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.
The attention over this aspect is growing but has never been thoroughly addressed by the existing literature.
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.
This dataset has also been used to evaluate the best QA systems available at the state of the art.