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RDF2vec is an embedding technique for representing knowledge graph entitiesin a continuous vector space. In this paper, we investigate the effect ofmaterializing implicit A-box axioms induced by subproperties, as well assymmetric and transitive properties. While it might be a reasonable assumptionthat such a materialization before computing embeddings might lead to betterembeddings, we conduct a set of experiments on DBpedia which demonstrate thatthe materialization actually has a negative effect on the performance ofRDF2vec. In our analysis, we argue that despite the huge body of work devotedon completing missing information in knowledge graphs, such missing implicitinformation is actually a signal, not a defect, and we show examplesillustrating that assumption.

More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings