Resources

The main public website for the Accelerate BASSO Network Center, providing an overview of the network, its goals, projects, and working groups.

An ontology of entities relevant to addictions and their treatment.

Presents an ontology-based method for formalizing and encoding patient colonoscopy preparation using BPMN and OWL2 for automated tool development.

Analyzes a Llama 3-based approach for translating axioms from ontologies, presented at the KBC-LM'24 workshop at the International Semantic Web Conference (ISWC) 2024.

Background and aims Ontologies are increasingly employed to help find, use and synthesise information, but methods for using them to annotate documents and datasets remain in their infancy in the behavioural and social sciences. The Behavioural Research UK DEMO-DATA project aimed to develop a prototype schema for annotating datasets in behavioural and social sciences. Methods A case-study dataset (the 'Smoking Toolkit Study'), used to inform an Agent-Based Model of trajectories in cigarette smoking and cessation in England, was chosen for annotation using two ontologies - The Behaviour Change Intervention Ontology (BCIO) and the Addiction Ontology (AddictO). The data set included 21 variables representing information about sociodemographic and tobacco and nicotine use attributes of the study population. A preliminary version of the schema for linking variables to ontology classes was developed as a basis for annotating each variable in the dataset. This was applied and revised iteratively until it was judged by an expert panel of domain experts and modellers to represent the variables sufficiently accurately to enable searching for and integration of data. Results The prototype Schema for Ontology-based Dataset Annotation (SODA) version 1.0 was developed over seven iterations. Variables were represented by an 'object property'|'ontology class' expression (e.g., 'has characteristic'|'extent of social smoking') together with information about the data types (e.g., numbers, ontology subclasses, or Boolean values), measurement source, unit of measurement, any coding or data transformations and whether or not the variable was fully characterised by the annotation. The prototype schema was applied successfully to the smoking dataset with 15 new ontology classes being created as required. Conclusions A prototype schema for annotating behavioural and social science datasets was developed and successfully applied to a dataset on smoking in England using ontology relations and classes. The next step is to further develop and evaluate the schema by application to case studies with a range of users and other datasets.

A Discourse-based community of practice for sharing knowledge and discussion about cancer prevention ontology tools.

Project website for APRICOT (Advancing Prevention Research in Cancer through Ontology Tools), which develops and refines ontologies to standardize behavioral science constructs in cancer prevention research.

An ontology for annotating and synthesising evidence about behaviour change interventions.

Project website for BSO-AD (Standardizing and Harmonizing Behavioral and Social Science Research Factors in Alzheimer's Disease through Ontology-Based Approaches), which develops ontology-driven methods to standardize BSSR data related to Alzheimer's Disease.

A curated subset of BioPortal providing a focused browsing experience for behavioural and social sciences ontologies.

The Behavioural and Social Sciences Ontology Foundry — a community of interoperable ontologies relevant to the behavioural and social sciences.

Describes the development of an ontology of pain, related to dental care fear and anxiety research. Published at the International Conference on Biomedical Ontology (ICBO) 2024, Enschede, The Netherlands.

An ontology for contextualised physical activity and exercise recommendations.

An ontology of affective phenomena including emotions, moods, and related processes.

Empowering Alzheimer's caregivers with conversational AI: a novel approach for enhanced communicatio. Published in npj Biomedical Innovations.

Abstract Medical coding is essential for healthcare operations yet remains predominantly manual, error-prone (up to 20%), and costly (up to $18.2 billion annually). Although large language models (LLMs) have shown promise in natural language processing, their application to medical coding has produced limited accuracy. In this study, we evaluated whether fine-tuning LLMs with specialized ICD-10 knowledge can automate code generation across clinical documentation. We adopted a two-phase approach: initial fine-tuning using 74,260 ICD-10 code–description pairs, followed by enhanced training to address linguistic and lexical variations. Evaluations using a proprietary model (GPT-4o mini) on a cloud platform and an open-source model (Llama) on local GPUs demonstrated that initial fine-tuning increased exact matching from <1% to 97%, while enhanced fine-tuning further improved performance in complex scenarios, with real-world clinical notes achieving 69.20% exact match and 87.16% category match. These findings indicate that domain-specific fine-tuned LLMs can reduce manual burdens and improve reliability.

Background Developing behaviour change interventions able to tackle major challenges such as non-communicable diseases or climate change requires effective and efficient use of scientific evidence. The Human Behaviour-Change Project (HBCP) aims to improve evidence synthesis in behavioural science by compiling intervention reports and annotating them with an ontology to train information extraction and prediction algorithms. The HBCP used smoking cessation as the first 'proof of concept' domain but intends to extend its methodology to other behaviours. The aims of this paper are to (i) assess the extent to which methods developed for annotating smoking cessation intervention reports were generalisable to a corpus of physical activity evidence, and (ii) describe the steps involved in developing this second HBCP corpus. Methods The development of the physical activity corpus involved: (i) reviewing the suitability of smoking cessation codes already used in the HBCP, (ii) defining the selection criteria and scope, (iii) identifying and screening records for inclusion, and (iv) annotating intervention reports using a code set of 200+ entities from the Behaviour Change Intervention Ontology. Results Stage 1 highlighted the need to modify the smoking cessation behavioural outcome codes for application to physical activity. One hundred physical activity intervention reports were reviewed, and 11 physical activity experts were consulted to inform the adapted code set. Stage 2 involved narrowing down the scope of the corpus to interventions targeting moderate-to-vigorous physical activity. In stage 3, 111 physical activity intervention reports were identified, which were then annotated in stage 4. Conclusions Smoking cessation annotation methods developed as part of the HBCP were mostly transferable to the physical activity domain. However, the codes applied to behavioural outcome variables required adaptations. This paper can help anyone interested in building a body of research to develop automated evidence synthesis methods in physical activity or for other behaviours.

An ontology covering mental health concepts including disorders, symptoms, and treatments.

Describes the initial development of the ODFA ontology for representing dental fear, anxiety, and phobia concepts to support standardised research. Published at the International Conference on Biomedical Ontology (ICBO) 2024, Enschede, The Netherlands.

An ontology for mental functioning including cognitive and emotional processes.

A report describing how ontologies support science and its application to real-world problems. It details how ontologies function, how they can be engineered to better support the behavioral sciences, and the resources needed to sustain their development and use to help ensure the maximum benefit from investment in behavioral science research.

Project website for ODFA (Ontology of Dental care-related Fear, Anxiety, and/or Phobia), which develops a standardized ontology for representing negative responses to dental treatment.

An ontology for social entities including roles, organizations, and demographic categories.

An ontology for representing dental fear, anxiety, and phobia concepts to support standardised research.

A Python-based web platform for collaborative editing of OWL ontologies using spreadsheet interfaces. Makes ontology curation accessible to domain experts without requiring deep knowledge of OWL syntax. Supports real-time collaboration, GitHub integration, search across loaded ontologies, quality validation, and merge conflict resolution.

The GitHub repository for the PHASES project (Promoting Health Aging through Semantic Enrichment of Solitude Research), containing ontology source files and related resources.

Project website for PHASES (Promoting Health Aging through Semantic Enrichment of Solitude Research), which develops interoperable ontologies and tools to advance understanding of solitude's role in healthy aging.

An ontology for representing relationships between different behaviours.

Changing behaviour at scale is needed to address the major challenges facing humanity: from preventing and treating disease to tackling the climate crisis. Developing effective interventions to achieve this requires efficient generation and use of scientific evidence. The Human Behaviour-Change Project developed an extensive ontology of behaviour change interventions, their contexts and mechanisms of action to organise global evidence about behaviour change and predict intervention outcomes in novel behaviour change scenarios. The APRICOT (Advancing Prevention Research In Cancer through Ontology Tools) project extends this work by developing (i) ontologies covering health-related behaviours, (ii) a Community of Practice for ontologies in the social and behavioural sciences, and (iii) tools and resources to make ontologies more useable and useful. It will also develop methods to apply and integrate ontologies with real-world data related to the social and environmental determinants of health and inequalities, and to facilitate their uptake in behavioural science practice.

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.