Due to the large amount of available e-learning data, identifying relevant information from e-learning data presents significant challenges. A recommendation system is a popular solution to provide relevant data to any user but it also faces challenges such as scalability, processing large volumes of data, addressing the cold start problem, predicting personalized recommendations, and providing an adaptive recommendation, etc. In this paper, we present an efficient knowledge graph-based recommendation framework, which can provide personalized e-learning recommendations to existing or new target learners without having enough historical data of that target learner. The proposed framework includes five modules i.e. Data Module, Knowledge Graph Representation Module, Community Building Module, Graph Embedding Module, and Recommendation Module. It is based on knowledge graphs to deal with huge amounts of data and to identify the hidden relationships between data. The proposed framework aims to provide personalized recommendations to learners, it utilizes a clustering-based community generation model for better identifying the interests or preferences of learners.