Knowledge

Almost all societal grand challenges, whether concerning the environment, health, well-being, or the development of sustainable economic models, have at their heart a need to understand people’s behaviour. However, uniting data and insights across disparate fields requires an explicit and shared understanding of concepts, variables, and ideas (e.g., how to characterise and differentiate behaviours). Ontologies provide a mechanism for creating this explicit and shared understanding and are starting to be developed and used in the social and behavioural sciences. This paper proposes an online co-design approach to use and develop ontologies of behaviour to specify the characteristics of behaviour (e.g., habitual, changeable, effortless) and studies that investigate behaviour as part of a project designed to understand how behaviours are related. We report on our experience of collaborative co-development of ontologies using real-time interactive tools and reflect on the benefits and challenges of our approach. We also offer a set of recommendations for researchers interested in applying such methods to co-develop ontologies. The work contributes to efforts to understand the characteristics of behaviour and enable these to be used to understand questions about behaviour (e.g., is poor sleep associated with greater engagement in habitual behaviours?).

This article presents a case study showing the development of a chatbot, named Selene, in a Software-as-a-Service platform for behavioral analysis using Retrieval-Augmented Generation (RAG) integrating domain-specific knowledge and enforcing adherence to organizational rules to improve response quality. Selene is designed to provide deep analyses and practical recommendations that help users optimize organizational behavioral development. To ensure that the RAG pipeline had updated information, we implemented an Extract, Transform, and Load process that updated the knowledge base of the pipeline daily and applied prompt engineering to ensure compliance with organizational rules and directives, using GPT-4 as the underlying language model of the chatbot, which was the state-of-the-art model at the time of deployment. We followed the Generative AI Project Life Cycle Frameworkas the basic methodology to develop this system. To evaluate Selene, we used the DeepEval library, showing that it provides appropriate responses and aligning with organizational rules. Our results show that the system achieves high answer relevancy in 78% of the test cases achieved and a complete absence of bias and toxicity issues. This work provides practical insights for organizations deploying similar knowledge-based chatbot systems.

In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional methods for constructing lexical networks, however, are resource-intensive, relying heavily on expert lexicographers. Leveraging GPT-4o, a large language model (LLM), our study presents an automated, scalable approach to creating multi-relational Japanese lexical networks for the general Japanese language. This study builds on previous methods of integrating synonyms but extends to other relations such as hyponymy, hypernymy, meronymy, and holonomy. Using a combination of structured prompts and graph-based data storage, the model extracts detailed lexical relationships, which are then systematically validated and encoded. Results reveal a substantial expansion in network size, with over 155,000 nodes and 700,000 edges, enriching Japanese lexical associations with nuanced hierarchical and associative layers. Comparisons with WordNet show substantial alignment in relation types, particularly with soft matching, underscoring the model’s efficacy in reflecting the multifaceted nature of lexical semantics. This work contributes a versatile framework for constructing expansive lexical resources that hold promises for enhancing NLP tasks and educational applications across various languages and domains.