RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings
This enables the application of embeddings for very large knowledge graphs in scenarios where it was previously infeasible, representing an incremental improvement.
The paper tackles the high computational cost of embedding entire knowledge graphs by introducing RDF2Vec Light, which generates vectors only for a subset of entities, resulting in significantly lower runtime and hardware requirements.
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.