将RDF知识图谱语义融入GNN，提升推荐系统性能
Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
- 结合RDF对象属性拓扑与数据属性内容信息
- 多场景实验验证语义丰富度显著提升推荐效果
- 适合构建开放数据云上智能推荐系统的研究者
图神经网络（GNN）已显著推动推荐系统发展。然而，尽管已有上千个符合W3C标准RDF的知识图谱，其丰富的语义信息尚未在基于GNN的推荐系统中充分应用。为此，我们提出一种全面集成RDF知识图谱与GNN的方法，同时利用RDF对象属性的拓扑信息和数据属性的内容信息。重点评估多种GNN模型，分析不同语义特征初始化方式及图结构异质性对推荐任务的影响。在包含数百万节点的多个推荐场景下进行实验，结果表明，挖掘RDF知识图谱的语义丰富性能显著提升推荐系统性能，并为面向链接开放数据云的GNN推荐系统奠定基础。代码与数据已在GitHub公开：https://github.com/davidlamprecht/rdf-gnn-recommendation
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have substantially advanced the field of recommender systems. However, despite the creation of more than a thousand knowledge graphs (KGs) under the W3C standard RDF, their rich semantic information has not yet been fully leveraged in GNN-based recommender systems. To address this gap, we propose a comprehensive integration of RDF KGs with GNNs that utilizes both the topological information from RDF object properties and the content information from RDF datatype properties. Our main focus is an in-depth evaluation of various GNNs, analyzing how different semantic feature initializations and types of graph structure heterogeneity influence their performance in recommendation tasks. Through experiments across multiple recommendation scenarios involving multi-million-node RDF graphs, we demonstrate that harnessing the semantic richness of RDF KGs significantly improves recommender systems and lays the groundwork for GNN-based recommender systems for the Linked Open Data cloud. The code and data are available on our GitHub repository: https://github.com/davidlamprecht/rdf-gnn-recommendation
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