Modelling a science domain for the purposes of thematically categorizing the research work and enabling better browsing and search can be a daunting task, especially if a specialized taxonomy or ontology does not exist for this domain. Elsevier, the largest academic publisher, faces this challenge often, for the needs of supporting the journals submission system, but also for supplying ScienceDirect and Scopus, two flagship platforms of the company, with sufficient metadata, such as conceptual labels that characterize the research works, which can improve the user experience in browsing and searching the literature. In this paper we describe an Elsevier in-use case study of learning appropriate domain labels from a collection of 6, 357 full text articles in the neurology domain, exploring different document representations and clustering mechanisms. Besides the baseline approaches for document representation (e.g., bag-of-words) and their variations (e.g., n-grams), we employ a novel in-house methodology which produces conceptual fingerprints of the research articles, starting from a general domain taxonomy, such as the Medical Subject Headings (MeSH). A thorough empirical evaluation is presented, using a variety of clustering mechanisms and several validity indices to evaluate the resulting clusters. Our results summarize the best practices in modelling this specific domain and we report on the advantages and disadvantages of using the different clustering mechanisms and document representations that were examined, with the aim to learn appropriate conceptual labels for this domain.