Within software applications, user experience is greatly improved when user interface (UI) personalisation is possible, and even more so when recommender systems can help users find the set of settings best suited for their skills and goals. In this paper, we suggest that such recommender systems should be based on ontologies dedicated to describing both software traits and user preferences, an example of which is the Ontology-based Web Application Generation ontology (OWAO) that specifies what web applications and their UI are. The key scientific contribution of our approach is ontowalk2vec, an algorithm that maps instances of ontologies to feature vectors (embeddings) that can be later on used for classification purposes, a process inherent to recommender systems. In addition to OWAO, we validate ontowalk2vec on two other significant ontologies, namely MUTAG and DBpedia, where we demonstrate it outperforms existing techniques. We finally discuss how using ontowalk2vec on OWAO can form the basis of personalised UI recommender systems, stressing, in particular, the importance of properly setting the many hyperparameters that typically characterise embedding-generation algorithms.