Large Language Models (LLMs) have revolutionized how we interact with information, but they sometimes struggle with complex reasoning, especially in specialized fields. Imagine an LLM that not only understands language but also grasps the intricate relationships between concepts within a specific domain. This is the promise of ontology-enhanced representation learning, a novel technique explored in recent research. Ontologies, structured knowledge bases that define relationships between concepts, offer a powerful way to inject domain expertise into LLMs. This research introduces a method to infuse this structured knowledge into LLMs, boosting their understanding and reasoning abilities. The process involves using a generative LLM like GPT-3.5-turbo to create definitions for concepts within a chosen ontology. These definitions, along with the ontology's structure, are then used to fine-tune an embedding-LLM through contrastive learning. This technique trains the LLM to recognize relationships between similar and dissimilar concepts, effectively embedding the ontology's knowledge into the LLM's understanding of language. The researchers tested this approach using the biomedical disease ontology MONDO, demonstrating significant improvements in the LLM's ability to evaluate the similarity between sentences related to diseases. Interestingly, this improvement didn't come at the cost of performance in other areas. The LLM retained its general language understanding while gaining specialized knowledge in biomedicine. This breakthrough opens exciting possibilities for developing more accurate and reliable domain-specific LLMs. Imagine LLMs that can reason like medical experts, legal scholars, or financial analysts. While this research focuses on biomedicine, the technique can be applied to any field with a well-defined ontology. Future research will explore different LLM architectures and ontologies, paving the way for a new generation of knowledge-infused LLMs. This approach holds immense potential for transforming how we use LLMs in specialized fields, offering a path towards more knowledgeable and reliable AI systems.