This paper presents a scholarly Knowledge Graph Question Answering (KGQA) that answers bibliographic natural language questions by leveraging a large language model (LLM) in a few-shot manner. The model initially identifies the top-n similar training questions related to a given test question via a BERT-based sentence encoder and retrieves their corresponding SPARQL. Using the top-n similar question-SPARQL pairs as an example and the test question creates a prompt. Then pass the prompt to the LLM and generate a SPARQL. Finally, runs the SPARQL against the underlying KG - ORKG (Open Research KG) endpoint and returns an answer. Our system achieves an F1 score of 99.0%, on SciQA - one of the Scholarly-QALD-23 challenge benchmarks.

翻译：本文提出了一种学术知识图谱问答（KGQA）方法，通过以少样本方式利用大语言模型（LLM）来回答文献自然语言问题。该模型首先基于BERT句子编码器识别与给定测试问题最相似的前n个训练问题，并检索其对应的SPARQL查询。利用这前n个相似问题-SPARQL对作为示例，结合测试问题构建提示。随后将提示输入LLM以生成SPARQL查询。最后在底层知识图谱——开放研究知识图谱（ORKG）端点上执行该SPARQL查询并返回答案。我们的系统在SciQA（Scholarly-QALD-23挑战赛基准之一）上取得了99.0%的F1分数。