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.

翻译：以RDF表示的知识图谱能够通过本体对实体及其关系进行建模。近年来，利用知识图谱进行信息建模引起了广泛关注。在推荐系统中，物品和用户可被映射并集成至知识图谱中，从而表示用户与物品之间更多的关联与联系。约束推荐系统基于通过显式利用约束来挖掘深层推荐知识的核心思想，以识别相关推荐结果。当与知识图谱结合时，约束推荐系统在约束集方面可获多项优势。本文研究并提出了一种面向车辆购销领域的基于RDF知识图谱的约束推荐系统构建方法。实验结果表明，该方法能够高效地识别符合用户偏好的推荐结果。