We present a novel hybrid approach for Word Sense Disambiguation (WSD) which makes use of a relational formalism to represent instances and background knowledge. It is built using Inductive Logic Programming techniques to combine evidence coming from both sources during the learning process, producing a rule-based WSD model. We experimented with this approach to disambiguate 7 highly ambiguous verbs in EnglishPortuguese translation. Results showed that the approach is promising, achieving an average accuracy of 75%, which outperforms the other machine learning techniques investigated (66%).