Classic machine learning tasks, such as clustering and link prediction, can be applied to Knowledge Graphs making used of the so-called graph embeddings, mathematical vector representations of the nodes present within the graph structure. Often, the data structure of Knowledge Graphs in Digital Humanities is at the same time versatile and complex, challenging the machine learning tasks. In this work, we compare algorithms on two different subgraphs extracted from a large knowledge graph developed in the cultural heritage domain: one is randomly selected while the other is built to maximise the triple density. Using the European Olfactory Knowledge Graph (EOKG) as use-case, we show that embedding dense subgraph can improve the performances of state-of-art algorithms.