Abstract
Logical reasoning over incomplete knowledge graphs to answer complex logical queries is a challenging task. With the emergence of new entities and relations in constantly evolving KGs, inductive logical reasoning over KGs has become a crucial problem. However, previous PLMs-based methods struggle to model the logical structures of complex queries, which limits their ability to generalize within the same structure. In this paper, we propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs. It encodes linearized query structures and entities using pre-trained language models to find answers. For structure modeling of complex queries, we design stepwise instructions that implicitly prompt PLMs on the execution order of geometric operations in each query. We further separately model different geometric operations (i.e., projection, intersection, and union) on the representation space using a pre-trained encoder with additional attention and maxout layers to enhance structured modeling. We conduct experiments on two inductive logical reasoning datasets and three transductive datasets. The results demonstrate the effectiveness of our method on logical reasoning over KGs in both inductive and transductive settings.
Abstract (translated)
在不完整的知识图谱上进行逻辑推理以回答复杂的逻辑查询是一项挑战性的任务。随着不断进化的KGs中新实体和新关系的出现，对KG上的基于归纳的逻辑推理 became a crucial问题。然而，以前的基于PLM的方法 struggle 来建模复杂的查询逻辑结构，这限制了他们在同结构内泛化的能力。在本文中，我们提出了一种基于结构建模的文字编码框架，以处理KG上的基于归纳的逻辑推理。它使用预先训练的语言模型来线性化查询结构和实体，以找到答案。为了建模复杂的查询结构，我们设计了一系列步骤指令，暗示PLM在每个查询中的几何操作执行顺序。我们还使用一个带有额外注意力和最大输出层的预先训练编码器来分别建模不同的几何操作，以增强结构建模。我们研究了两个基于归纳的逻辑推理数据集和三个基于转换的数据集。结果证明了我们在基于和基于转换 settings 下对KG上的逻辑推理推理的有效性。
URL
https://arxiv.org/abs/2305.13585
