Knowledge Graphs popularity has been rapidly growing in last years. All that
knowledge is available for people to query it through the many online databases
on the internet. Though, it would be a great achievement if non-programmer
users could access whatever information they want to know. There has been a lot
of effort oriented to solve this task using natural language processing tools
and creativity encouragement by way of many challenges. Our approach focuses on
assuming a correct entity linking on the natural language questions and
training a GPT model to create SPARQL queries from them. We managed to isolate
which property of the task can be the most difficult to solve at few or
zero-shot and we proposed pre-training on all entities (under CWA) to improve
the performance. We obtained a 62.703% accuracy of exact SPARQL matches on
testing at 3-shots, a F1 of 0.809 on the entity linking challenge and a F1 of
0.009 on the question answering challenge.

我们的研究致力于通过自然语言处理工具和创意鼓励来解决大众用户查询知识图谱的问题，我们的方法是通过正确的实体链接和训练 GPT 模型从自然语言问题中创建 SPARQL 查询，我们获得了在测试中 3 次查询命中准确度为 62.703%，实体链接挑战的 F1 值为 0.809，问题回答挑战的 F1 值为 0.009。