  Knowledge graph embedding approaches represent nodes and edges of graphs as
mathematical vectors. Current approaches focus on embedding complete knowledge
graphs, i.e. all nodes and edges. This leads to very high computational
requirements on large graphs such as DBpedia or Wikidata. However, for most
downstream application scenarios, only a small subset of concepts is of actual
interest. In this paper, we present RDF2Vec Light, a lightweight embedding
approach based on RDF2Vec which generates vectors for only a subset of
entities. To that end, RDF2Vec Light only traverses and processes a subgraph of
the knowledge graph. Our method allows the application of embeddings of very
large knowledge graphs in scenarios where such embeddings were not possible
before due to a significantly lower runtime and significantly reduced hardware
requirements.
