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

• URL: http://arxiv.org/abs/2506.08743v1
• Date: Tue, 10 Jun 2025 12:38:24 GMT
• Title: Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
• Authors: Michael Färber, David Lamprecht, Yuni Susanti,
• Abstract summary: We propose a comprehensive integration of RDF knowledge graphs with Graph Neural Networks (GNNs)<n>Our main focus is an in-depth evaluation of various GNNs.<n>We demonstrate that harnessing the semantic richness of RDF KGs significantly improves recommender systems.
• Score: 9.408189129889006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

Related papers

• A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph [14.12873271435375]
  Graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation. We provide a novel evaluation perspective on GNNs-based recommendation, which investigates the impact of the graph topology on the recommendation performance.
  arXiv  Detail & Related papers  (2024-08-21T16:34:53Z)
• Linear-Time Graph Neural Networks for Scalable Recommendations [50.45612795600707]
  The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. We propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches.
  arXiv  Detail & Related papers  (2024-02-21T17:58:10Z)
• Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems [4.026354668375411]
  We propose a novel reinforcement learning-based message passing framework for recommender systems. This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning. Our results show that our proposed framework significantly enhances the recent base model, improving nDCG and Recall by up to 63.7% and 42.9%, respectively.
  arXiv  Detail & Related papers  (2023-02-21T09:47:27Z)
• A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features [61.92791503017341]
  Graph Neural Networks (GNNs) with numerical node features and graph structure as inputs have demonstrated superior performance on various supervised learning tasks with graph data. The best models for such data types in most standard supervised learning settings with IID (non-graph) data are not easily incorporated into a GNN. Here we propose a robust stacking framework that fuses graph-aware propagation with arbitrary models intended for IID data.
  arXiv  Detail & Related papers  (2022-06-16T22:46:33Z)
• Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems [5.030752995016985]
  Graph4Rec is a universal toolkit that unifies the paradigm to train GNN models. We conduct a systematic and comprehensive experiment to compare the performance of different GNN models.
  arXiv  Detail & Related papers  (2021-12-02T07:56:13Z)
• Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction [123.20238648121445]
  We propose a new self-supervised learning framework, Graph Information Aided Node feature exTraction (GIANT) GIANT makes use of the eXtreme Multi-label Classification (XMC) formalism, which is crucial for fine-tuning the language model based on graph information. We demonstrate the superior performance of GIANT over the standard GNN pipeline on Open Graph Benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-29T19:55:12Z)
• DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN [59.160401038969795]
  We propose differentiable sampling on Knowledge Graph for Recommendation with GNN (DSKReG) We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems.
  arXiv  Detail & Related papers  (2021-08-26T16:19:59Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)
• Graph Neural Networks in Recommender Systems: A Survey [21.438347815928918]
  In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information. Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems.
  arXiv  Detail & Related papers  (2020-11-04T12:57:47Z)
• Hierarchical BiGraph Neural Network as Recommendation Systems [0.0]
  We propose a hierarchical approach of using GNNs as recommendation systems and structuring the user-item features using a bigraph framework. Our experimental results show competitive performance with current recommendation system methods and transferability.
  arXiv  Detail & Related papers  (2020-07-27T18:01:41Z)
• Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks [183.97265247061847]
  We leverage graph signal processing to characterize the representation space of graph neural networks (GNNs) We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. We also study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.
  arXiv  Detail & Related papers  (2020-03-08T13:02:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.