  The recent success of Large Language Models (LLM) in a wide range of Natural
Language Processing applications opens the path towards novel Question
Answering Systems over Knowledge Graphs leveraging LLMs. However, one of the
main obstacles preventing their implementation is the scarcity of training data
for the task of translating questions into corresponding SPARQL queries,
particularly in the case of domain-specific KGs. To overcome this challenge, in
this study, we evaluate several strategies for fine-tuning the OpenLlama LLM
for question answering over life science knowledge graphs. In particular, we
propose an end-to-end data augmentation approach for extending a set of
existing queries over a given knowledge graph towards a larger dataset of
semantically enriched question-to-SPARQL query pairs, enabling fine-tuning even
for datasets where these pairs are scarce. In this context, we also investigate
the role of semantic "clues" in the queries, such as meaningful variable names
and inline comments. Finally, we evaluate our approach over the real-world Bgee
gene expression knowledge graph and we show that semantic clues can improve
model performance by up to 33% compared to a baseline with random variable
names and no comments included.
