  Knowledge-intensive tasks pose a significant challenge for Machine Learning
(ML) techniques. Commonly adopted methods, such as Large Language Models
(LLMs), often exhibit limitations when applied to such tasks. Nevertheless,
there have been notable endeavours to mitigate these challenges, with a
significant emphasis on augmenting LLMs through Knowledge Graphs (KGs). While
KGs provide many advantages for representing knowledge, their development costs
can deter extensive research and applications. Addressing this limitation, we
introduce a framework for enriching embeddings of small-scale domain-specific
Knowledge Graphs with well-established general-purpose KGs. Adopting our
method, a modest domain-specific KG can benefit from a performance boost in
downstream tasks when linked to a substantial general-purpose KG. Experimental
evaluations demonstrate a notable enhancement, with up to a 44% increase
observed in the Hits@10 metric. This relatively unexplored research direction
can catalyze more frequent incorporation of KGs in knowledge-intensive tasks,
resulting in more robust, reliable ML implementations, which hallucinates less
than prevalent LLM solutions.
  Keywords: knowledge graph, knowledge graph completion, entity alignment,
representation learning, machine learning
