Enhanced GAT: Expanding Receptive Field with Meta Path-Guided RDF Rules for Two-Hop Connectivity

Neuro-Symbolic Artificial Intelligence is an emerging field that combines neural networks and symbolic reasoning to tackle complex tasks, such as ontology reasoning, i.e. inferring new facts that are not expressed in an ontology. However, the integration of symbolic reasoning in neural networks for efficient reasoning over very large and complex ontologies still remains relatively unexplored. Therefore, this paper introduces a scalable neural-symbolic method called 2-Hop GAT for reasoning over large and complex ontologies, which is an extension of the Graph Attention Network (GAT), leveraging meta paths of two hop to capture transitivity. By extending GAT to include nodes that are two hops away, the proposed method achieves enhanced reasoning capabilities. Additionally, the Filtered 2-Hop GAT variant is presented, which adds a filtering mechanism to guide meta paths of two hop to include two RDF rules. Namely, (1) subclass transitivity: if A is a subclass of B, and B is a subclass of C, then A is also a subclass of C, and (2) if A is a type of B and B is a subclass of C, then A is also a type of C. This paper reports experimental results using the datasets from the SemRec Challenge at ISWC 2023, demonstrating the effectiveness of the proposed methods. The latter approach shows promising results, achieving a Hits@5 score of 0.752 and a Hits@10 score of 0.803 for the class subsumption task.