Walk this Way! Entity Walks and Property Walks for RDF2vec
This work addresses the need for better entity similarity and relatedness in knowledge graph embeddings, but it appears incremental as it builds on existing RDF2vec methods.
The authors tackled the problem of improving RDF2vec knowledge graph embeddings by introducing two new walk extraction strategies, e-walks and p-walks, which focus on entity structure and neighborhood respectively, and conducted a preliminary evaluation with 12 variants.
RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm word2vec for computing vector representations for entities. In this poster, we introduce two new flavors of walk extraction coined e-walks and p-walks, which put an emphasis on the structure or the neighborhood of an entity respectively, and thereby allow for creating embeddings which focus on similarity or relatedness. By combining the walk strategies with order-aware and classic RDF2vec, as well as CBOW and skip-gram word2vec embeddings, we conduct a preliminary evaluation with a total of 12 RDF2vec variants.