Knowledge graphs (KGs) have become an essential component of neuro-symbolic AI research. A KG is a uniform source of information in which physical-world entities are represented as vertices of a directed edge-labeled graph. In the context of representation learning, edge labels of a KG are called relations, and its edges are called facts or triples [5]. KGs can be leveraged in a great variety of AI applications. Over the past decade, many KG Embedding Models (KGEMs) have been developed for that purpose [5]. By representing entities and relations as numeric structures in a vector space, KGEMs provide a way to integrate both symbolic and sub-symbolic knowledge, enabling efficient processing and reasoning over complex and heterogeneous data. Most KGEMs are evaluated against datasets that are derived from Freebase, a (now archived) public KG containing millions of entities and billions of facts.