  Knowledge Graphs (KGs) store information in the form of (head, predicate,
tail)-triples. To augment KGs with new knowledge, researchers proposed models
for KG Completion (KGC) tasks such as link prediction; i.e., answering (h; p;
?) or (?; p; t) queries. Such models are usually evaluated with averaged
metrics on a held-out test set. While useful for tracking progress, averaged
single-score metrics cannot reveal what exactly a model has learned -- or
failed to learn. To address this issue, we propose KGxBoard: an interactive
framework for performing fine-grained evaluation on meaningful subsets of the
data, each of which tests individual and interpretable capabilities of a KGC
model. In our experiments, we highlight the findings that we discovered with
the use of KGxBoard, which would have been impossible to detect with standard
averaged single-score metrics.
