  Knowledge graph embedding models (KGEMs) have gained considerable traction in
recent years. These models learn a vector representation of knowledge graph
entities and relations, a.k.a. knowledge graph embeddings (KGEs). Learning
versatile KGEs is desirable as it makes them useful for a broad range of tasks.
However, KGEMs are usually trained for a specific task, which makes their
embeddings task-dependent. In parallel, the widespread assumption that KGEMs
actually create a semantic representation of the underlying entities and
relations (e.g., project similar entities closer than dissimilar ones) has been
challenged. In this work, we design heuristics for generating protographs --
small, modified versions of a KG that leverage RDF/S information. The learnt
protograph-based embeddings are meant to encapsulate the semantics of a KG, and
can be leveraged in learning KGEs that, in turn, also better capture semantics.
Extensive experiments on various evaluation benchmarks demonstrate the
soundness of this approach, which we call Modular and Agnostic SCHema-based
Integration of protograph Embeddings (MASCHInE). In particular, MASCHInE helps
produce more versatile KGEs that yield substantially better performance for
entity clustering and node classification tasks. For link prediction, using
MASCHinE substantially increases the number of semantically valid predictions
with equivalent rank-based performance.
