Horoyah: A Knowledge Graph-based Framework for Election Monitoring in Low and Middle-Income Countries

In many low and middle-income countries (LMICs), electoral processes are plagued by dysfunctions that undermine democratic principles and public trust. These issues, ranging from political corruption to a lack of transparency, often lead to post-election violence and contested results. Civil society organizations (CSOs) play a crucial role in mitigating these problems through election monitoring, but often lack the tools for effective, large-scale data collection and analysis. This paper introduces Horoyah, an innovative platform designed to empower civil society and the general public by leveraging knowledge graphs to promote transparency and accountability in election monitoring. Horoyah provides a structured framework for collecting, aggregating, and analysing citizen-reported election results from polling stations. Structuring voting data within a knowledge graph enables the platform to facilitate the identification of trends, inconsistencies, and potential irregularities, thereby enhancing data accuracy and providing an auditable record of the electoral process. We outline its design and demonstrate its application through a use case based on the 2020 presidential elections in Guinea, West Africa. We conclude by discussing the potential of such tools to fortify democratic practices while also considering the significant challenges of disinformation, cybersecurity, and the digital divide that they raise.