Lemmatization, which reduces words to their root forms, plays a key role in tasks such as information retrieval, text indexing, and machinelearning-based language models. However, a key research challenge for low-resourced languages such as the Somali is the lack of human-annotated lemmatization datasets and reliable ground truth to underpin accurate morphological analysis and training relevant NLP models. To address this problem, we developed the first large-scale, purpose-built Somali lemmatization lexicon, coupled with a crowdsourcing platform for ongoing expansion. The system leverages Somali’s agglutinative and derivational morphology, encompassing over5,584 root words and 78,629 derivative forms, each annotated with part-of-speech tags. For data validation purpose, we have devised a pilot lexicon-based lemmatizer integrated with rule-based logic to handle out-of-vocabulary terms. Evaluation on a 294-document corpuscovering news articles, social media posts, and short messages shows lemmatization accuracies of 51.27% for full articles, 44.14% forexcerpts, and 59.51% for short texts such as tweets. These results demonstrate that combining lexical resources, POS tagging, and rulebased strategies provides a robust and scalable framework for addressing morphological complexity in Somali and other low-resource languages