Multi-hop knowledge graph (KG) reasoning has been widely studied in recent years to provide interpretable predictions on missing links with evidential paths. Most previous works use reinforcement learning (RL) based methods that learn to navigate the path towards the target entity. However, these methods suffer from slow and poor convergence, and they may fail to infer a certain path when there is a missing edge along the path. Here we present SQUIRE, the first Sequence-to-sequence based multi-hop reasoning framework, which utilizes an encoder-decoder Transformer structure to translate the query to a path. Our framework brings about two benefits: (1) It can learn and predict in an end-to-end fashion, which gives better and faster convergence; (2) Our Transformer model does not rely on existing edges to generate the path, and has the flexibility to complete missing edges along the path, especially in sparse KGs. Experiments on standard and sparse KGs show that our approach yields significant improvement over prior methods, while converging 4x-7x faster.

翻译：近些年来,人们广泛研究了多光学知识图(KG)的推理,以提供对证据路径缺失路段的可解释预测。大多数以往的工程都使用了基于强化学习(RL)的方法,这些方法可以学习如何引导目标实体。然而,这些方法的趋同速度缓慢且差强人意,它们可能无法推导出一条路径。这里我们展示了基于序列到序列的第一个多光学推理框架SQUIRE,这是第一个基于序列到序列的多光学框架,它利用编码-脱钩变异器结构将查询转换成一条路径。我们的框架带来了两个好处:(1) 它可以以端到端的方式学习和预测,从而更好和更快的趋同速度;(2) 我们的变异器模型不依靠现有边缘来生成路径,并且具有完成路径上缺失的边缘的灵活性,特别是在稀疏的 KGs。在标准上和稀疏的KGs上进行的实验表明,我们的方法比先前的方法大改进,同时将4x-7x速度加速。