Ontologies provide structured vocabularies and relationships essential for organizing biological knowledge, yet their symbolic nature limits integration with modern machine learning methods. Leveraging recent advances in graph neural networks (GNNs) and transformer-based language models, we present on2vec, a toolkit developed during the DBCLS BioHackathon 2025 for generating vector embeddings from OWL ontologies. on2vec integrates structural information from ontology hierarchies with semantic features from textual annotations using HuggingFace Sentence Transformers, producing domain-aware embeddings suitable for downstream biomedical applications and ontology-based reasoning tasks.