  Knowledge graphs, represented in RDF, are able to model entities and their
relations by means of ontologies. The use of knowledge graphs for information
modeling has attracted interest in recent years. In recommender systems, items
and users can be mapped and integrated into the knowledge graph, which can
represent more links and relationships between users and items.
Constraint-based recommender systems are based on the idea of explicitly
exploiting deep recommendation knowledge through constraints to identify
relevant recommendations. When combined with knowledge graphs, a
constraint-based recommender system gains several benefits in terms of
constraint sets. In this paper, we investigate and propose the construction of
a constraint-based recommender system via RDF knowledge graphs applied to the
vehicle purchase/sale domain. The results of our experiments show that the
proposed approach is able to efficiently identify recommendations in accordance
with user preferences.
