Ontology matching, particularly when applied to large and noisy web-based datasets, presents significant challenges related to both volume and veracity. In this paper, we introduce a novel hybrid methodology designed to address these complexities effectively. Our approach integrates traditional text matching techniques, string similarity metrics (Levenshtein distance and TF-IDF cosine similarity), Sentence-BERT embeddings, and a Siamese neural network based on LSTM architectures. We focus on the practical task of matching company names extracted from online job postings to formal company registries in Italy and Germany. To assess our methodology, we conducted extensive empirical experiments matching distinct company names from Lightcast job postings with entries from the Orbis dataset. After initial normalization procedures, we combined exact matching and similarity-based approaches, enhancing robustness through deep learning-driven embeddings from our Siamese network. Our evaluation shows that while traditional methods (particularly TF-IDF-based matching) performed reliably, the integration of deep learning provided additional discriminative power, especially beneficial in handling noisy, inconsistent naming conventions. Our proposed hybrid methodology significantly improves ontology matching accuracy, thus offering an effective solution to large-scale, real-world entity resolution tasks.