  Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of
predicting facts for new, previously unseen entities based on context
information. Although new entities can be integrated by retraining the model
from scratch in principle, such an approach is infeasible for large-scale KGs,
where retraining is expensive and new entities may arise frequently. In this
paper, we propose and describe a large-scale benchmark to evaluate
semi-inductive LP models. The benchmark is based on and extends Wikidata5M: It
provides transductive, k-shot, and 0-shot LP tasks, each varying the available
information from (i) only KG structure, to (ii) including textual mentions, and
(iii) detailed descriptions of the entities. We report on a small study of
recent approaches and found that semi-inductive LP performance is far from
transductive performance on long-tail entities throughout all experiments. The
benchmark provides a test bed for further research into integrating context and
textual information in semi-inductive LP models.
