🫠Conclusions

In our examination of ChatGPT's efficacy across various stages of ontology searching and modeling, we observed notable strengths particularly concerning the elucidation of biases. ChatGPT demonstrated proficiency in defining biases, occasionally surpassing existing literature by providing expanded insights. Moreover, its capacity to craft scenarios proved valuable for meticulous analysis of the multifaceted dimensions inherent within biases. However, we also encountered limitations. At times, ChatGPT exhibited unfamiliarity with certain biases, and its tendency to formulate scenarios that predominantly led to positive resolutions diverged from the bias's core definition, thereby introducing extraneous elements into the analysis. Regarding assistance in formulating ontology classes and properties, initial responses displayed precision. Yet, as we delved deeper into creating bias-specific properties and intricacies, ChatGPT's effectiveness diminished, as it tended to stray from the primary and synchronic structure of the bias architecture.

The usage of Ontology Design Patterns (ODPs) proved both intriguing and demanding. While we identified several advantageous patterns suitable for segments of our ontologies, the greatest challenge lay in seamlessly integrating them into a coherent and structured framework. It became evident that many patterns were either overly specialized or overly generalized for our particular applications. Regrettably, within the realm of psychology, there exists a dearth of ODPs tailored to our needs. Consequently, we resorted to amalgamating disparate elements from various patterns to construct a bespoke solution tailored to our domain. Employing ODPs across multiple levels and domains necessitates meticulous attention to detail, as haphazard utilization can engender internal conflicts. To mitigate such issues, it often becomes imperative to employ sub-properties, thereby ensuring consistency and harmony within the ontology.

It is important to emphasize the indispensable nature of collaborative efforts in ensuring the coherence of the overarching structure of ontologies. Through coordinated teamwork, we were able to establish common guidelines, facilitating the integration of disparate components without encountering conflicts. It is necessary to recognize that the process of modeling remains inherently subjective and susceptible to the biases of the creator. Thus, the involvement of domain experts assumes significant importance, particularly in delineating cognitive biases with precision. Domain experts play a pivotal role in elucidating the optimal level of abstraction necessary for effectively encapsulating biases within the ontology. Their expertise aids in navigating the intricate nuances inherent to bias definitions, thereby enriching the ontology with comprehensive and accurate representations.