Despite the impressive advancements in Large Language Models (LLMs), their ability to perform reasoning and provide explainable outcomes remains a challenge, underscoring the continued relevance of ontologies in certain areas, particularly due to the reasoning and validation capabilities of ontologies. Ontology modelling and semantic search, due to their inherent complexity, still demand considerable human effort and expertise. Addressing this gap, our paper introduces the problem of ontology text alignment, which involves finding the most relevant axioms with respect to the given reference text. We propose an advanced Retrieval Augmented Generation framework that leverages BERT models and generative LLMs, together with ontology semantic enhancement based on atomic decomposition. Additionally, we have developed benchmarks in geology and biomedical areas. Our evaluation demonstrates the positive impact of our framework.