Enhancing Knowledge Graph Completion Through Neural-Symbolic Fusion: A Novel Graph Distillation Framework with Semantic Web Integration

Knowledge graphs (KGs) have emerged as fundamental structures for organizing interconnected data across diverse domains in the semantic web ecosystem. However, most real-world KGs remain incomplete, limiting their effectiveness in downstream applications. This paper presents a novel neural-symbolic framework that integrates Graph Neural Network (GNN) distillation with Abstract Probabilistic Interaction Modeling (APIM) to address critical challenges in knowledge graph completion (KGC). Our approach tackles the over-smoothing problem in deep GNNs through iterative message-feature filtering while incorporating semantic web technologies for enhanced knowledge representation. The proposed framework introduces a unified architecture that combines symbolic reasoning with deep learning to leverage complementary benefits from both paradigms. We evaluate our methodology on standard benchmarks including WN18RR and FB15K-237 datasets, achieving significant performance improvements over baseline models. Experimental results demonstrate a 10.9% improvement in Hits@1 metric compared to state-of-the-art approaches with Mean Reciprocal Rank (MRR) scores of 0.523 on FB15K-237 and 0.440 on WN18RR. The framework effectively addresses semantic similarity challenges while maintaining computational efficiency through knowledge graph embeddings that preserve hierarchical relationships [4][5]. Our contributions include the introduction of automatic embedding dimension learning for hierarchical entities, novel semantic enrichment techniques for information retrieval and comprehensive evaluation protocols that ensure fair comparison across different model architectures. The research bridges the gap between semantic web technologies and machine learning communities, providing practical solutions for real-world knowledge graph applications with validated experimental results and reproducible methodologies.