mlsanityData to 7 Oct 2026

AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning

arXiv 2407.18735 ↗First posted 26 Jul 2024cs.LG cs.AI cs.IRcited by 1

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Abstract (arXiv)

In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content-based features -- i.e., features based on RDF datatype properties -- and topology-based features -- i.e., features based on RDF object properties. Characterized by automated feature extraction, AutoRDF2GML makes it possible even for users less familiar with RDF and SPARQL to generate data representations ready for graph machine learning tasks, such as link prediction, node classification, and graph classification. Furthermore, we present four new benchmark datasets for graph machine learning, created from large RDF knowledge graphs using our framework. These datasets serve as valuable resources for evaluating graph machine learning approaches, such as graph neural networks. Overall, our framework effectively bridges the gap between the Graph Machine Learning and Semantic Web communities, paving the way for RDF-based machine learning applications.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 55 references that are in the map. First posted on arXiv 26 Jul 2024.

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21 of 55 references.

References · 21

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9 more
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systemscited by 1