Abductive reasoning is the process of making educated guesses to provide explanations for observations. Although many applications require the use of knowledge for explanations, the utilization of abductive reasoning in conjunction with structured knowledge, such as a knowledge graph, remains largely unexplored. To fill this gap, this paper introduces the task of complex logical hypothesis generation, as an initial step towards abductive logical reasoning with KG. In this task, we aim to generate a complex logical hypothesis so that it can explain a set of observations. We find that the supervised trained generative model can generate logical hypotheses that are structurally closer to the reference hypothesis. However, when generalized to unseen observations, this training objective does not guarantee better hypothesis generation. To address this, we introduce the Reinforcement Learning from Knowledge Graph (RLF-KG) method, which minimizes differences between observations and conclusions drawn from generated hypotheses according to the KG. Experiments show that, with RLF-KG's assistance, the generated hypotheses provide better explanations, and achieve state-of-the-art results on three widely used KGs.

翻译：溯因推理是一种基于有根据的猜测来解释观测结果的过程。尽管许多应用场景需要借助知识进行解释，但与结构化知识（如知识图谱）相结合的溯因推理仍鲜有探索。为填补这一空白，本文提出了复杂逻辑假设生成任务，作为基于知识图谱进行溯因逻辑推理的初步步骤。该任务旨在生成一个复杂的逻辑假设，使其能够解释一组观测结果。研究发现，监督训练的生成模型能够生成结构上更接近参考假设的逻辑假设。然而，当泛化到未见过的观测结果时，该训练目标并不能保证生成更优质的假设。为此，我们引入了基于知识图谱的强化学习（RLF-KG）方法，该方法通过最小化观测结果与基于知识图谱从生成假设推导出的结论之间的差异来优化生成效果。实验表明，在RLF-KG的辅助下，生成的假设能够提供更优的解释，并在三个广泛使用的知识图谱上取得了最先进的结果。