Universal Preprocessing Operators for Embedding Knowledge Graphs with
  Literals
Patryk Preisner Heiko Paulheim
Knowledge graph embeddings are dense numerical representations of entities in
a knowledge graph (KG). While the majority of approaches concentrate only on
relational information, i.e., relations between entities, fewer approaches
exist which also take information about literal values (e.g., textual
descriptions or numerical information) into account. Those which exist are
typically tailored towards a particular modality of literal and a particular
embedding method. In this paper, we propose a set of universal preprocessing
operators which can be used to transform KGs with literals for numerical,
temporal, textual, and image information, so that the transformed KGs can be
embedded with any method. The results on the kgbench dataset with three
different embedding methods show promising results.