Graph Joint Representation Learning for Sound and Scalable Instantiation

Abstract

Knowledge graph reasoning is a fundamental problem in artificial intelligence, where rule-based methods provide strong interpretability but suffer from exponential symbolic search complexity, while existing neuro-symbolic approaches struggle to model complex Horn rules with rich topologies. We propose Rule–Graph Joint Representation Learning (RGRS), a unified neuro-symbolic framework that integrates rule-level reasoning with graph representation learning for scalable Horn rule instantiation. RGRS introduces logically constrained rule embeddings and dominance-aware subgraph representations, enabling embedding-guided structural pruning while preserving correctness through exact symbolic verification. By coupling continuous structural abstraction with deterministic matching in a filter–verify pipeline, RGRS effectively reduces the combinatorial search space without compromising logical semantics. Extensive experiments on large-scale knowledge graphs show that RGRS achieves consistently high accuracy with significant efficiency gains over symbolic and neuro-symbolic baselines, providing a principled bridge between symbolic reasoning and embedding-based efficiency.