The scientific literature and internal research documents every institution produces is a key source of information for the members of the institution itself. To access this material effectively and to retrieve the information needed going beyond the keyword-based approach, a Transformer-based language model tailored on the semiconductor supply chain domain is employed together with the same domain ontology -- the Digital Reference [1] -- to build a document retrieval system over the pool of documents of the Infineon Corporate Supply Chain Innovation department. The further pre-training of the Bidirectional Encoder Representations from Transformers (BERT) model [2] on a text corpus based on the semiconductor supply chain literature is used to empower SentenceBERT [3] for sentence embeddings creation.