The article provides an overview of knowledge-based recommender systems (KBRs), which differ from traditional collaborative filtering and content-based filtering approaches by leveraging semantic user preference knowledge, item knowledge, and recommendation knowledge to identify relevant items. KBRs are particularly useful in scenarios where users specify and revise their preferences, and recommendations are determined based on constraints or attribute-level similarity metrics. The authors explain different KBR techniques using a case study from survey software services and outline future research directions. Key contributions include enhancing existing overviews, providing a working example, and discussing open research areas. The article also covers hybrid and group recommender systems, advanced techniques integrating machine learning, and handling inconsistent user preferences.The article provides an overview of knowledge-based recommender systems (KBRs), which differ from traditional collaborative filtering and content-based filtering approaches by leveraging semantic user preference knowledge, item knowledge, and recommendation knowledge to identify relevant items. KBRs are particularly useful in scenarios where users specify and revise their preferences, and recommendations are determined based on constraints or attribute-level similarity metrics. The authors explain different KBR techniques using a case study from survey software services and outline future research directions. Key contributions include enhancing existing overviews, providing a working example, and discussing open research areas. The article also covers hybrid and group recommender systems, advanced techniques integrating machine learning, and handling inconsistent user preferences.