FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph Reasoning
This addresses a bottleneck in knowledge graph reasoning for AI applications by enabling true handling of all first-order logical operations and support for various feature spaces.
The paper tackles the limited logical reasoning ability and feature generalization of existing knowledge graph reasoning models by proposing the Feature-Logic Embedding framework (FLEX), which significantly outperforms state-of-the-art methods on benchmark datasets.
Current best performing models for knowledge graph reasoning (KGR) introduce geometry objects or probabilistic distributions to embed entities and first-order logical (FOL) queries into low-dimensional vector spaces. They can be summarized as a center-size framework (point/box/cone, Beta/Gaussian distribution, etc.). However, they have limited logical reasoning ability. And it is difficult to generalize to various features, because the center and size are one-to-one constrained, unable to have multiple centers or sizes. To address these challenges, we instead propose a novel KGR framework named Feature-Logic Embedding framework, FLEX, which is the first KGR framework that can not only TRULY handle all FOL operations including conjunction, disjunction, negation and so on, but also support various feature spaces. Specifically, the logic part of feature-logic framework is based on vector logic, which naturally models all FOL operations. Experiments demonstrate that FLEX significantly outperforms existing state-of-the-art methods on benchmark datasets.