  SPARQL is a highly powerful query language for an ever-growing number of
Linked Data resources and Knowledge Graphs. Using it requires a certain
familiarity with the entities in the domain to be queried as well as expertise
in the language's syntax and semantics, none of which average human web users
can be assumed to possess. To overcome this limitation, automatically
translating natural language questions to SPARQL queries has been a vibrant
field of research. However, to this date, the vast success of deep learning
methods has not yet been fully propagated to this research problem. This paper
contributes to filling this gap by evaluating the utilization of eight
different Neural Machine Translation (NMT) models for the task of translating
from natural language to the structured query language SPARQL. While
highlighting the importance of high-quantity and high-quality datasets, the
results show a dominance of a CNN-based architecture with a BLEU score of up to
98 and accuracy of up to 94%.
