Abstract
RDF2vec (and other techniques) provide embedding vectors for knowledge graphs. While we have used a simple node classification task so far, this chapter introduces a few datasets and three common benchmarks for embedding methods—i.e., SW4ML, GEval, and DLCC—and shows how to use them for comparing different variants of RDF2vec. The novel DLCC benchmark allows us to take a closer look at what RDF2vec vectors actually represent, and to analyze what proximity in the vector space means for them.
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Notes
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The desired size classes can be configured in the framework.
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Since the classification tasks are balanced, random guessing would yield an accuracy of 0.5.
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Paulheim, H., Ristoski, P., Portisch, J. (2023). Benchmarking Knowledge Graph Embeddings. In: Embedding Knowledge Graphs with RDF2vec. Synthesis Lectures on Data, Semantics, and Knowledge. Springer, Cham. https://doi.org/10.1007/978-3-031-30387-6_3
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DOI: https://doi.org/10.1007/978-3-031-30387-6_3
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