Query Structure Modeling for Inductive Logical Reasoning Over Knowledge
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Siyuan Wang Zhongyu Wei Meng Han Zhihao Fan Haijun Shan Qi Zhang Xuanjing Huang
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.