Sam Smith

Background Ontologies are essential for structuring biomedical knowledge, supporting semantic integration, reasoning, and data interoperability. In vaccinology, ontology population is particularly critical, as vaccines span diverse domains. A well-defined Vaccine Ontology enables consistent knowledge representation, integration across datasets, and supports applications such as decision support, literature mining, and semantic search. However, manual ontology population is tedious, time-consuming, and difficult to maintain in this dynamically evolving domain, underscoring the need for automated or semi-automated population approaches. Methods We present a semi-automated pipeline that uses BERTopic, an advanced topic modeling approach that leverages Bidirectional Encoder Representations from Transformers (BERT)-based embeddings to extract ontology relevant concepts from biomedical text. To evaluate the effectiveness of this automated approach, the method is applied to Plotkin’s Vaccines corpus, a leading reference text in vaccinology that synthesizes scientific, clinical, and policy perspectives on vaccines. The workflow integrates multiple natural language processing (NLP) components: document preprocessing with spaCy part-of-speech tagging and vectorization, sentence embeddings generated by a lightweight transformer model (all-MiniLM-L6-v2), dimensionality reduction with Uniform Manifold Approximation and Projection (UMAP), clustering with Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and topic representation. To guide topic discovery toward vaccine-relevant concepts and filter irrelevant terms, the pipeline incorporates a curated set of vaccine-focused terms derived from the existing Vaccine Ontology (VO) as seed words to influence topic representations, while preserving the unsupervised nature of the clustering process. To enhance interpretability, the pipeline employs Keyword extraction using BERT embeddings (KeyBERT) for automatic keyword-based labeling, supplemented with disambiguated descriptive labels, and Bidirectional and Auto-Regressive Transformer (BART) summarization for topic-level summaries. The resulting hierarchical topic structures are further refined through a tree-merging module that unifies multiple topic hierarchies into a coherent ontology-like representation. The extracted topics are reviewed by the Subject Matter Experts (SMEs) to filter irrelevant terms and then mapped to Vaccine Ontology, a well-established ontology to assess their relevance and coverage, demonstrating how automated methods can reduce the labor-intensive effort required for manual ontology population. Results The script is customized to generate a varying number of topics and keywords. In this study, the top 50 topics with 10 keywords per topic were extracted for each chapter of Plotkin’s vaccines. The pipeline produced coherent topic clusters representing key themes in vaccinology, including immune mechanisms, pathogen-specific vaccines, and vaccine types. The hierarchical tree-merging process is used to illustrate how semantically related concept groupings emerge and can suggest potential ontology subdivisions. This serves as a visualization of conceptual relationships derived from the data and is particularly helpful for SMEs to review, interpret, and validate candidate concepts. Conclusions This study demonstrates the feasibility of BERTopic-driven, a semi-automated approach for extracting ontology-relevant concepts from biomedical texts. The method was evaluated using a foundational vaccinology corpus and assessed against an existing, well-developed VO to determine the relevance and coverage of the extracted topics. Mapping the topics to the established ontology enabled identification of concept alignments and irrelevant terms, which were subsequently reviewed by SMEs. The results show that the proposed approach can effectively surface meaningful, ontology-relevant concepts while significantly reducing the time and effort for manual population, thereby providing a scalable strategy for supporting ontology maintenance and enrichment.

The term “credential” encompasses educational certificates, degrees, certifications, and government-issued licenses. An occupational credential is a verification of an individual’s qualification or competence issued by a third party with relevant authority. Job seekers often leverage such credentials as evidence that desired qualifications are satisfied by their holders. Many U.S. education and workforce development organizations have recognized the importance of credentials for employment and the challenges of understanding the value of credentials. In this study, we identified and ontologically defined credential and credential-related terms at the textual and semantic levels based on the Occupation Ontology (OccO), a BFO-based ontology. Different credential types and their authorization logic are modeled. We additionally defined a high-level hierarchy of credential related terms and relations among many terms, which were initiated in concert with the Alabama Talent Triad (ATT) program, which aims to connect learners, earners, employers and education/training providers through credentials and skills. To our knowledge, our research provides for the first time systematic ontological modeling of the important domain of credentials and related contents, supporting enhanced credential data and knowledge integration in the future.