1 research outputs found
Global and local evaluation of link prediction tasks with neural embeddings
We focus our attention on the link prediction problem for knowledge graphs,
which is treated herein as a binary classification task on neural embeddings of
the entities. By comparing, combining and extending different methodologies for
link prediction on graph-based data coming from different domains, we formalize
a unified methodology for the quality evaluation benchmark of neural embeddings
for knowledge graphs. This benchmark is then used to empirically investigate
the potential of training neural embeddings globally for the entire graph, as
opposed to the usual way of training embeddings locally for a specific
relation. This new way of testing the quality of the embeddings evaluates the
performance of binary classifiers for scalable link prediction with limited
data. Our evaluation pipeline is made open source, and with this we aim to draw
more attention of the community towards an important issue of transparency and
reproducibility of the neural embeddings evaluations.Comment: Accepted to 4th Semantic Deep Learning (SemDeep-4) Workshop at the
ISWC 201