  In this work we will show that language models with less than one billion
parameters can be used to translate natural language to SPARQL queries after
fine-tuning. Using three different datasets ranging from academic to real
world, we identify prerequisites that the training data must fulfill in order
for the training to be successful. The goal is to empower users of semantic web
technology to use AI assistance with affordable commodity hardware, making them
more resilient against external factors.
