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：溯因推理是通过做出有根据的猜测来为观察结果提供解释的过程。尽管许多应用需要使用知识进行解释，但将溯因推理与结构化知识（如知识图谱）结合使用的方法在很大程度上仍未得到探索。为填补这一空白，本文提出了复杂逻辑假设生成任务，作为在知识图谱中进行溯因逻辑推理的初步尝试。在该任务中，我们的目标是生成一个复杂的逻辑假设，使其能够解释一组观察结果。研究发现，经过监督训练的生成模型能够生成在结构上更接近参考假设的逻辑假设。然而，当推广到未见过的观察结果时，这种训练目标并不能保证生成更好的假设。为解决这一问题，我们引入了基于知识图谱的强化学习方法，该方法根据知识图谱最小化观察结果与从生成假设中得出的结论之间的差异。实验表明，在RLF-KG的辅助下，生成的假设能提供更好的解释，并在三个广泛使用的知识图谱上取得了最先进的结果。