DOI: https://doi.org/10.1145/3726302.3730271
Access to and consumption of agricultural research findings remains a challenge for Kiswahili-speaking farmers and extension officers in Tanzania due to the predominance of English in agriculture scholarly publications. To address this challenge, the Mkulima repository, a digital collection of over 600 Swahili agricultural publications, was developed at the Sokoine University of Agriculture to provide agriculture knowledge in Kiswahili. However, its current structure limits effective retrieval and accessibility, given the type of its intended audience, smallholder farmers. This work-in-progress aims to improve access to agricultural knowledge in Kiswahili through a hybrid model that integrates a domain-specific Knowledge Graph (KG) with Retrieval-Augmented Generation (RAG), an approach that combines traditional retrieval with generative language models for producing informed answers. The project`s findings are aimed to contribute to AI-driven retrieval systems for low-resource languages, with results targeted for submission as a paper to SIGIR 2026.