Code review is a crucial but often complex, subjective, and time-consuming task in software development. Early automation efforts relied on knowledge-based systems (KBS), which offer precise rule-driven feedback but are limited in handling complex, context-dependent issues. More recent approaches have focused on learning-based systems (LBS), particularly fine-tuned pre-trained language models, enabling broader issue coverage but frequently sacrificing precision. In this proposal, we introduce a hybrid approach that integrates structured external knowledge with LBS to generate accurate and comprehensive code reviews. We explore multiple integration strategies at different stages of the large language model (LLM) pipeline and evaluate them against standalone KBS and LBS baselines on a real-world dataset. Our results show consistent improvements in both accuracy and coverage.