With the fast pace of reporting around the globe from various sources, event extraction has increasingly become an important task in NLP. The use of pre-trained language models (PTMs) has become popular to provide contextual representation for downstream tasks. This work aims to pre-train language models that enhance event extraction accuracy. To this end, we propose an Event-Based Knowledge (EBK) masking approach to mask the most significant terms in the event detection task. These significant terms are based on an external knowledge source that is curated for the purpose of event detection for the Arabic language. The proposed approach improves the classification accuracy of all the 9 event types. The experimental results demonstrate the effectiveness of the proposed masking approach and encourage further exploration.