Answering complex logical queries over incomplete knowledge graphs (KGs) is challenging. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neural networks. However, they are bottlenecked by the inability to share world knowledge to improve logical reasoning, thus resulting in suboptimal performance. In this paper, we propose a complex logical reasoning schema over knowledge graphs upon large language models (LLMs), containing a curriculum-based logical-aware instruction tuning framework, named LACT. Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition, to stimulate the reasoning capability of LLMs. To address the difficulty gap among different types of complex queries, we design a simple and flexible logic-aware curriculum learning framework. Experiments across widely used datasets demonstrate that LACT has substantial improvements~(brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art. Our code and model will be released at GitHub and huggingface soon.

翻译：在不完全知识图谱上回答复杂逻辑查询具有挑战性。以往研究主要聚焦于学习实体/关系嵌入，并通过各类神经网络模拟一阶逻辑算子。然而，这些方法因无法共享世界知识以改进逻辑推理而受到瓶颈，导致性能欠佳。本文提出一种基于大语言模型的知识图谱复杂逻辑推理框架，包含名为LACT的课程式逻辑感知指令调优机制。具体而言，我们通过二叉树分解增强任意一阶逻辑查询，以激发大语言模型的推理能力。为应对不同类型复杂查询的难度差异，我们设计了一种简单灵活的、符合逻辑的课程学习框架。在广泛使用的数据集上的实验表明，LACT相较于先进方法取得了显著改进（平均MRR评分提升+5.5%），达到新的最优性能。我们的代码和模型将很快在GitHub和Huggingface上发布。