In the fields of machine learning and artificial intelligence, recommendation systems (RS) or recommended engines are commonly used. In today's world, recommendation systems based on user preferences assist consumers in making the best decisions without depleting their cognitive resources. They can be applied to a variety of things, including search engines, travel, music, movies, literature, news, gadgets, and dining. A lot of people utilize RS on social media sites like Facebook, Twitter, and LinkedIn, and it has proven beneficial in corporate settings like those at Amazon, Netflix, Pandora, and Yahoo. There have been numerous proposals for recommender system variations. However, certain techniques result in unfairly recommended things due to biased data because there are no established connections between the items and consumers. In order to solve the challenges mentioned above for new users, we propose in this work to employ Content-based Filtering (CBF) and Collaborative Filtering (CF) with semantic relationships to capture the relationships as knowledge-based book recommendations to readers in a digital library. When proposing things, patterns are more discriminative than single phrases. To capture the similarity of the books that the new user had retrieved, the patterns were grouped in a semantically equivalent manner using the Clustering method. The effectiveness of the suggested model is examined through a series of extensive tests employing Information Retrieval (IR) evaluation criteria. Recall Precision and F-Measure, two of the three widely used performance measuring metrics, were employed. The findings demonstrate that the suggested model performs noticeably better than cutting-edge models.