A Knowledge Engineering Framework Addressing High Incidence of Farmer Suicides

This study addresses the pressing issue of farmer suicides in India, a problem that has escalated significantly since 1995, with over 0.3 million recorded cases. While scholars and activists have proposed various factors contributing to these suicides, quantitative insights into the influence and probabilistic significance of these factors remain elusive. Consequently, we introduce a pioneering two-tier knowledge engineering framework to tackle this challenge. In the first tier, we employ natural language processing to gather a comprehensive array of suicide-causing factors from news articles and blog posts on the World Wide Web. In the second tier, we undertake a meticulous analysis of the causal factors by calculating probabilities and categorising the identified causal factors into distinct groups: social, economic, socio-economic, nature-related, health-related, and governmental policies. Our analysis reveals that health-related causal factors account for over 50% of farmer suicides, while economic, social, and socio-economic factors contribute to 26%. By meticulously investigating these causal elements, our approach has the potential to substantially mitigate future instances of farmer suicides.