We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day -- attributes that are out-of-scope for current KGQA systems. BYOKG draws inspiration from the remarkable ability of humans to comprehend information present in an unseen KG through exploration -- starting at random nodes, inspecting the labels of adjacent nodes and edges, and combining them with their prior world knowledge. In BYOKG, exploration leverages an LLM-backed symbolic agent that generates a diverse set of query-program exemplars, which are then used to ground a retrieval-augmented reasoning procedure to predict programs for arbitrary questions. BYOKG is effective over both small- and large-scale graphs, showing dramatic gains in QA accuracy over a zero-shot baseline of 27.89 and 58.02 F1 on GrailQA and MetaQA, respectively. On GrailQA, we further show that our unsupervised BYOKG outperforms a supervised in-context learning method, demonstrating the effectiveness of exploration. Lastly, we find that performance of BYOKG reliably improves with continued exploration as well as improvements in the base LLM, notably outperforming a state-of-the-art fine-tuned model by 7.08 F1 on a sub-sampled zero-shot split of GrailQA.

翻译：本文提出BYOKG，一种通用的知识图谱问答系统，该系统可在任意知识图谱上运行，无需人工标注的训练数据，且能在一天内完成部署——这些特性均超出当前知识图谱问答系统的能力范围。BYOKG的设计灵感来源于人类通过探索理解未知知识图谱的卓越能力：从随机节点出发，检查相邻节点与边的标签，并结合已有世界知识进行推理。在BYOKG中，探索过程借助基于大语言模型的符号智能体，该智能体生成多样化的查询-程序示例集，随后通过检索增强推理机制将这些示例用于支撑任意问题的程序预测。BYOKG在小型与大型图谱上均表现优异，在GrailQA和MetaQA数据集上的零样本基线F1值分别显著提升27.89和58.02。在GrailQA数据集上，我们进一步证明无监督的BYOKG优于有监督的上下文学习方法，验证了探索机制的有效性。最后，我们发现BYOKG的性能随着持续探索和基础大语言模型的改进而稳定提升，在GrailQA的零样本子集上以7.08 F1值的优势显著超越当前最优的微调模型。