  A booming amount of information is continuously added to the Internet as
structured and unstructured data, feeding knowledge bases such as DBpedia and
Wikidata with billions of statements describing millions of entities. The aim
of Question Answering systems is to allow lay users to access such data using
natural language without needing to write formal queries. However, users often
submit questions that are complex and require a certain level of abstraction
and reasoning to decompose them into basic graph patterns. In this short paper,
we explore the use of architectures based on Neural Machine Translation called
Neural SPARQL Machines to learn pattern compositions. We show that
sequence-to-sequence models are a viable and promising option to transform long
utterances into complex SPARQL queries.
