<ul><li>Graph Neural Networks (GNNs) have greatly advanced recommender systems, but have not fully utilized the semantic information in knowledge graphs (KGs) like RDF.</li><li>A new approach integrates RDF KGs with GNNs by leveraging topological and content information from RDF object and datatype properties.</li><li>The study evaluates various GNNs, analyzing how semantic feature initializations and graph structure heterogeneity affect their performance in recommendation tasks.</li><li>Experiments on multi-million-node RDF graphs show that leveraging RDF KGs' semantic richness significantly enhances recommender systems, paving the way for GNN-based systems in Linked Open Data cloud.</li></ul>

Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems

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