More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings
This work addresses a problem for knowledge graph embedding researchers by showing that missing implicit information can be beneficial rather than a defect, though it is incremental in nature.
The paper investigates the effect of materializing implicit A-box axioms on RDF2vec knowledge graph embeddings and finds that this materialization negatively impacts performance, as demonstrated through experiments on DBpedia.
RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitive properties. While it might be a reasonable assumption that such a materialization before computing embeddings might lead to better embeddings, we conduct a set of experiments on DBpedia which demonstrate that the materialization actually has a negative effect on the performance of RDF2vec. In our analysis, we argue that despite the huge body of work devoted on completing missing information in knowledge graphs, such missing implicit information is actually a signal, not a defect, and we show examples illustrating that assumption.