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.

使用大型语言模型，该研究论文介绍了一种学术知识图谱问答（KGQA）系统，通过少量示例解决书目自然语言问题。模型使用基于 BERT 的句子编码器来识别与给定测试问题相关的前 n 个相似训练问题，并检索它们对应的 SPARQL 查询。利用前 n 个相似问题 - SPARQL 对作为示例以及测试问题创建提示，并将其传递给大型语言模型以生成 SPARQL 查询。最后，在底层知识图谱（Open Research KG）端点上运行 SPARQL 查询并返回答案。该系统在 Scholarly-QALD-23 挑战基准中的 SciQA 数据集上获得了 99.0% 的 F1 分数。