  Using knowledge graph embedding models (KGEMs) is a popular approach for
predicting links in knowledge graphs (KGs). Traditionally, the performance of
KGEMs for link prediction is assessed using rank-based metrics, which evaluate
their ability to give high scores to ground-truth entities. However, the
literature claims that the KGEM evaluation procedure would benefit from adding
supplementary dimensions to assess. That is why, in this paper, we extend our
previously introduced metric Sem@K that measures the capability of models to
predict valid entities w.r.t. domain and range constraints. In particular, we
consider a broad range of KGs and take their respective characteristics into
account to propose different versions of Sem@K. We also perform an extensive
study to qualify the abilities of KGEMs as measured by our metric. Our
experiments show that Sem@K provides a new perspective on KGEM quality. Its
joint analysis with rank-based metrics offers different conclusions on the
predictive power of models. Regarding Sem@K, some KGEMs are inherently better
than others, but this semantic superiority is not indicative of their
performance w.r.t. rank-based metrics. In this work, we generalize conclusions
about the relative performance of KGEMs w.r.t. rank-based and semantic-oriented
metrics at the level of families of models. The joint analysis of the
aforementioned metrics gives more insight into the peculiarities of each model.
This work paves the way for a more comprehensive evaluation of KGEM adequacy
for specific downstream tasks.
