Hypergraph-Based Neural Architectures for Semantic Web Applications

Abstract

The Semantic Web’s ambition to create a globally interlinked data ecosystem hinges on accurately modeling complex, multifaceted relationships among entities. Traditional graph neural networks (GNNs), while effective for binary interactions, fall short when representing n‑ary associations intrinsic to Semantic Web datasets—such as multi‑authorship in scholarly networks, composite events in knowledge graphs, and intricate ontological constructs. Hypergraph Neural Networks (HGNNs) address this limitation by treating hyperedges as first‑class citizens, thereby capturing higher‑order connectivity without resorting to lossy pairwise decompositions. In this manuscript, we present a thorough investigation of hypergraph‑based neural architectures tailored for key Semantic Web tasks: ontology completion, link prediction, and relation extraction. We begin by formalizing hypergraph representations in the context of RDF and OWL standards, then survey seminal and state‑of‑the‑art HGNN variants—HGNN (Feng et al., 2019), HyperGCN (Yadati et al., 2019), and UniGNN (Tang et al., 2021)—highlighting their design choices in hyperedge normalization, message‑passing schemes, and computational scalability. Leveraging benchmark knowledge graphs (DBpedia and YAGO3), we conduct extensive experiments with controlled train/test splits and pre‑trained TransE embeddings to ensure reproducibility. Our statistical analysis—paired t‑tests across ten random splits—demonstrates that HGNNs achieve up to 5.8 % absolute improvement in link prediction hits@10 and up to 6.3 % absolute gain in ontology completion F1‑score compared to leading GNN baselines (p < 0.001). An ablation study further elucidates the impact of hyperedge cardinality and normalization strategies on model performance. Finally, we discuss practical integration strategies for Semantic Web pipelines, including hyperedge extraction from n‑ary RDF triples, and outline future directions such as dynamic hypergraphs for temporal reasoning and joint embedding of textual descriptions. Our findings affirm that hypergraph‑based learning offers a robust, scalable pathway to unlocking the full semantic richness of Web‑scale knowledge graphs.