  Knowledge graphs, on top of entities and their relationships, contain other
important elements: literals. Literals encode interesting properties (e.g. the
height) of entities that are not captured by links between entities alone. Most
of the existing work on embedding (or latent feature) based knowledge graph
analysis focuses mainly on the relations between entities. In this work, we
study the effect of incorporating literal information into existing link
prediction methods. Our approach, which we name LiteralE, is an extension that
can be plugged into existing latent feature methods. LiteralE merges entity
embeddings with their literal information using a learnable, parametrized
function, such as a simple linear or nonlinear transformation, or a multilayer
neural network. We extend several popular embedding models based on LiteralE
and evaluate their performance on the task of link prediction. Despite its
simplicity, LiteralE proves to be an effective way to incorporate literal
information into existing embedding based methods, improving their performance
on different standard datasets, which we augmented with their literals and
provide as testbed for further research.
