Abstract This paper explores the application of knowledgebased Word Sense Disambiguation systems to specifific domains, based on our state-of-the-art graphbased WSD system that uses the information in WordNet. Evaluation was performed over a publicly available domain-specifific dataset of 41 words related to Sports and Finance, comprising examples drawn from three corpora: one balanced corpus (BNC), and two domain-specifific corpora (news related to Sports and Finance). The results show that in all three corpora our knowledge-based WSD algorithm improves over previous results, and also over two state-of-the-art supervised WSD systems trained on SemCor, the largest publicly available annotated corpus. We also show that using related words as context, instead of the actual occurrence contexts, yields better results on the domain datasets, but not on the general one. Interestingly, the results are higher for domain-specifific corpus than for the general corpus, raising prospects for improving current WSD systems when applied to specifific domains