Behavioral and social science research (BSSR) is essential for understanding human behavior and informing interventions, policy, and health outcomes; however, such research faces persistent challenges related to fragmented knowledge, imprecise and inconsistent terminology, and limited interoperability across studies. Ontologies provide a promising solution by enabling standardized, machine-readable representations of concepts and relationships to support data integration, knowledge synthesis, and reproducibility. We present an initial overview of the ACCELERATE-BASSO Research Network, an NIH-supported consortium established to advance BSSR through ontology-driven approaches. Specifically, we describe the role of the Dissemination and Coordination Center (DCC) and the Best Practices Working Group (BPWG) in supporting a network of projects focusing on accelerating BSSR through ontology development and use. Based on the consortium’s early experiences, we summarize emerging ontology-driven practices, current interoperability efforts, and common methodological considerations identified across the participating projects. Rather than proposing a formal consensus framework, this work provides an initial consortium perspective on current ontology development activities and shared lessons learned. We also discuss the potential role of ontologies in supporting AI-related applications in BSSR. This paper provides an initial consortium perspective on ontology-driven approaches for advancing BSSR. By synthesizing early experiences, emerging practices, and shared challenges across multiple projects, it offers insights to inform future ontology development, interoperability, and community consensus while laying the foundation for more systematic evaluation and broader adoption within the BSSR community.

Background. Ontology development is a complex, iterative process that traditionally requires extensive collaboration between ontology developers and subject matter experts (SMEs). While effective, this manual approach is time-consuming, labor-intensive, and prone to cognitive bias. To streamline early-stage ontology development and uncover concepts that might be overlooked through manual review alone, we applied automated topic modeling with BERTopic to extract topics, keywords, topic labels, and summaries from The Handbook of Solitude: Psychological Perspectives on Social Isolation, Social Withdrawal, and Being Alone and Gerotranscendence: A Developmental Theory of Positive Aging . The extracted topic labels were used as candidate concepts for the Promoting Healthy Aging through Semantic Enrichment of Solitude Research (PHASES) Ontology. Methods. We implemented and compared two BERTopic pipelines: (1) the default configuration and (2) a custom preprocessing pipeline incorporating part-of-speech filtering and n-gram tuning.The pipeline is customized to flexibly extract any specified number of topics and keywords based on user-defined parameters. To compare and merge topic modeling outputs across solitude and gerotranscendence, we used semantic embeddings of topic labels, keywords, and summaries from the custom pipeline. Cosine similarity identified semantically matched topic pairs above a set threshold, enabling categorization and integration into a merged conceptual framework that bridges both domains. Results. From the solitude corpus, BERTopic generated 244 initial topics, which SME review refined to 32 high-quality topics with the custom pipeline and 46 with the default pipeline. For the gerotranscendence corpus, the pipeline produced 172 initial topics, refined to 33 (custom) and 32 (default) high-quality topics. Across both corpora, BERTopic contributed 90 ontology terms, 52 from the solitude corpus and 38 from the gerotranscendence corpus. Visual evaluations, including keyword score bar charts, hierarchical clustering dendrograms, and BART-generated summaries, revealed that the custom pipeline produced more fine-grained, domain-specific topics, while the default pipeline offered broader thematic coverage and clearer labels. Certain theory-laden concepts, however, required SME interpretive input. Conclusions. BERTopic provided an efficient, semi-automated approach for identifying candidate ontology terms from domain literature, supporting both breadth and specificity in concept capture. Integrating semantic similarity analysis across thematic domains revealed conceptual intersections and overlaps, enhancing the semantic foundation of the PHASES Ontology and offering a replicable method for cross-domain ontology development.

Research in STEM education is rapidly growing. Many terms in use have no shared meanings, making knowledge dissemination and accumulation difficult, if not impossible. With new terms constantly added to the literature, and often inconsistently used, there is a need for a common framework of STEM education terms. There is also a need to “take stock” of what has been accomplished in the field. To that end, we propose the adoption of standards and techniques from the field of ontology engineering to construct a shared vocabulary system that defines a universe of terms and formal relations among them, to address this challenge. We created a reference ontology, STEM Identity Ontology (STEMIO), and a domain ontology, Science Teacher Identity Ontology (STIO), to demonstrate how ontologies can be leveraged to improve existing STEM identity data and future research in the area. Ontology engineering has been successfully deployed across research in medicine, engineering, social science, and education; it has potential to facilitate research in STEM education.

This paper introduces two interoperable ontologies-SOLO (Solitude Ontology) and GERO (Gerotranscendence Ontology)-developed to formalize key psychological constructs relevant to healthy aging. Grounded in the Behavioral Change Intervention Ontology, the ontologies clarify distinctions between terms such as solitude, loneliness, self-transcendence, and gerotranscendence, and encode their realization conditions across the lifespan. By integrating validated psychometric instruments and supporting structured responses to competency questions, the ontologies enable semantic reasoning, cross-disciplinary data integration, and development of ontology-driven tools for aging research and intervention design.

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 (VO) 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. We present a semi-automated pipeline that uses Bidirectional Encoder Representations from Transformers and Topic Modeling (BERTopic) 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 via Class-based Term Frequency - Inverse Document Frequency (c-TF-IDF). To guide topic discovery toward vaccine-relevant concepts and filter irrelevant terms, the pipeline incorporates a curated set of vaccine-focused terms derived from an existing vaccine ontology 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. 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. 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 vaccine ontology 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.

BACKGROUND:Infectious diseases remain a critical global health challenge, and the integration of standardized ontologies plays a vital role in managing related data. The Infectious Disease Ontology (IDO) and its extensions, such as the Coronavirus Infectious Disease Ontology (CIDO), are essential for organizing and disseminating information related to infectious diseases. The COVID-19 pandemic highlighted the need for updating IDO and its virus-specific extensions. There is an additional need to update IDO extensions specific to bacteria, fungus, and parasite infectious diseases. METHODS:The "hub-and-spoke" methodology is adopted to generate pathogen-specific extensions of IDO: Virus Infectious Disease Ontology (VIDO), Bacteria Infectious Disease Ontology (BIDO), Mycosis Infectious Disease Ontology (MIDO), and Parasite Infectious Disease Ontology (PIDO). RESULTS:IDO is introduced before reporting on the scopes, major classes and relations, applications and extensions of IDO to VIDO, BIDO, MIDO, and PIDO. CONCLUSIONS:The creation of pathogen-specific reference ontologies advances modularization and reusability of infectious disease ontologies within the IDO ecosystem. Future work will focus on further refining these ontologies, creating new extensions, and developing application ontologies based on them, in line with ongoing efforts to standardize biological and biomedical terminologies for improved data sharing, quality, and analysis.

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

The Coronavirus Infectious Disease Ontology (CIDO) is a community-based ontology that supports coronavirus disease knowledge and data standardization, integration, sharing, and analysis. O ntologies, as the term is used in informatics, are structured vocabularies comprised of human-and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integration, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, pathogenesis, diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. Human coronaviruses have given rise to a series of major crises in global public health. Severe acute respiratory syndrome (SARS) emerged in China in November 2002, lasted for eight months and resulted in 8,098 confirmed human cases in 29 countries with 774 deaths (case-fatality rate: 9.6%) 1. Approximately ten years later in June 2012, the Middle East Respiratory Syndrome (MERS), another highly pathogenic coronavirus disease, was identified in Saudi Arabia. The MERS outbreak has caused 2,260 cases in 27 countries and 803 deaths (35.5%) 2. More recently, the World Health Organization (WHO) declared the Coronavirus Disease 2019 (COVID-19) outbreak as a pandemic on March 11, 2020, when there were 118,326 confirmed cases and 4,292 deaths. As of May 13, there have been over 4.4 million confirmed cases and over 295,000 deaths globally. Unfortunately, we still do not have available effective drugs and vaccines against these highly pathological coronaviruses. Extensive studies have been conducted on coronaviruses, the results of many of which exist in publicly available data repositories such as GEO 3. Publications concerning COVID-19 have exploded in recent months, and new clinical trials have been and are being conducted to develop drugs and vaccines against COVID-19, 1,430 of which have been registered in ClinicalTrials.gov as of May 13, 2020. As of May 13, 2020, a PubMed search of "SARS", "MERS", and "SARS-CoV-2 OR COVID-19" resulted in 12,993, 4,493 and 11,813 publications, respectively. A coordinated study of all such results would likely help with understanding and developing treatments for COVID-19. This coordinated study requires the integration of the large and exponentially growing data and research concerning COVID-19 to better understand its etiology, transmission, and pathogenesis mechanism. Moreover, we must be able to translate that understanding into rapid development of patient stratification.

This paper critically examines the relationship between Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO), offering a comprehensive discussion of their theoretical foundations, design patterns, and implementation practices. The paper highlights BFO’s commitment to realism, perspectivalism, fallibilism, and adequatism, and illustrates how these principles guide the representation of domain-specific entities within CCO. The modular structure of CCO is analyzed, emphasizing its eleven component ontologies and the hub-and-spoke strategy that promotes semantic integration across diverse domains. The paper also engages with practical challenges in distinguishing between TLOs, MLOs, and domain ontologies, proposing heuristic and formal criteria for delineating their scope. Using a dataset from the US Federal Aviation Administration, the paper demonstrates CCO’s modeling capabilities, particularly in integrating design specifications and real-world data. The study concludes by underscoring the importance of aligning data quality and semantic interoperability in ontology engineering, and it calls for sustained methodological rigor and collaboration to advance the BFO-CCO ecosystem.

This paper introduces a framework for representing information about entities that do not exist or may never exist, such as those involving fictional entities, blueprints, simulations, and future scenarios. Traditional approaches that introduce "dummy instances" or rely on modal logic are criticized, and a proposal is defended in which such cases are modeled using the intersections of actual types rather than specific non existent tokens. The paper positions itself within the Basic Formal Ontology and its realist commitments, emphasizing the importance of practical, implementable solutions over purely metaphysical or philosophical proposals, arguing that existing approaches to non existent entities either overcommit to metaphysical assumptions or introduce computational inefficiencies that hinder applications. By developing a structured ontology driven approach to unreal patterns, the paper aims to provide a useful and computationally viable means of handling references to hypothetical or non existent entities.

This work lays the foundations for a rigorous ontological characterization of love, addressing its philosophical complexity and scientific relevance, with particular emphasis on psychology and sociology, as well as highlighting ways in which such characterization enhances relevant AI based applications. The position defended here is that love is best understood as a concatenation of passive sensations (e.g., emotional arousal) and active evaluative judgments (e.g., perceiving the beloved as valuable), in the interest of balancing the involuntary aspects of love with its rational accountability. To provide a structured foundation, the paper draws on Basic Formal Ontology (BFO) and other applied ontological methods to differentiate various senses of love. This work engages with objections to the understanding of love as concatenation, particularly concerning the relationship between sensation and judgment. A causal correlation model is defended, ensuring that the affective and cognitive components are linked. By offering a precise and scalable ontological account, this work lays the foundation for future interdisciplinary applications, making love a subject of formal inquiry in ontology engineering, artificial intelligence, and the sciences.

. Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-eight classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using di ff erent top-level ontologies. to represent seven These cases, their and their accompanying focus statements are discussed in order to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using di ff erent top-level ontologies.

Ontologies have emerged to become critical to support data and knowledge representation, standardization, integration, and analysis. The SARS-CoV-2 pandemic led to the rapid proliferation of COVID-19 data, as well as the development of many COVID-19 ontologies. In the interest of supporting data interoperability, we initiated a community-based effort to harmonize COVID-19 ontologies. Our effort involves the collaborative discussion among developers of seven COVID-19 related ontologies, and the merging of four ontologies. This effort demonstrates the feasibility of harmonizing these ontologies in an interoperable framework to support integrative representation and analysis of COVID-19 related data and knowledge.

Constant advancements in biomanufacturing require operational frameworks that enable accurate comparisons between similar production processes for performance and cost optimization purposes. However, the extensive and heterogeneous nature of available data which is often presented in different formats, characterized in different ways, and reported in disparate units - poses a significant challenge to an efficient and effective comparative analysis. In this paper, the potential benefits of applying the biomanufacturing production process ontology to provide a common ground for addressing data heterogeneity issues are investigated in the context of process design optimization with respect to key performance indicators (KPIs). Using acetic acid fermentation as a case study, three semi-continuous processes involving different bacterial species and feedstock sources were analyzed. Existing industrial ontologies were utilized, and additional ontologies were developed to facilitate the normalization of notions and link data from multiple domains, including life cycle analysis, process, production, and materials. Following ontology development best practices, competency questions and respective queries were formulated to validate the ontology, with the aim to evaluate KPIs, including the material intensity and the number of fermentation cycles each microorganism can sustain. Results demonstrate that the ontology effectively resolved data inconsistencies, enabling more accurate comparisons and providing deeper insights into the differences in processes across various strains and conditions.

gist is an open-source, business-focused ontology actively developed by Semantic Arts. Its lightweight design and use of everyday terminology has made it a useful tool for kickstarting domain ontology development in a range of areas including finance, government, and pharmaceuticals. The Basic Formal Ontology (BFO) is an ISO/IEC standard upper ontology that has similarly found practical application across a variety of domains, especially biomedicine and defense. Given its demonstrated utility, BFO was recently adopted as a baseline standard in the U.S. Department of Defense and Intelligence Community. Because BFO sits at a higher level of abstraction than gist, we see an opportunity to align gist with BFO and get the benefits of both: one can kickstart domain ontology development with gist, all the while maintaining an alignment with the BFO standard. This paper presents such an alignment, which consists primarily of subclass relations from gist classes to BFO classes and includes some subproperty axioms. The union of gist, BFO, and this alignment is what we call "gistBFO." The upshot is that one can model instance data using gist and then instances of gist classes can be mapped to BFO. This not only achieves compliance with the BFO standard; it also enables interoperability with other domains already modeled using BFO. We describe a methodology for aligning gist and BFO, provide rationale for decisions we made about mappings, and detail a vision for future development.