With the increase of online information overload available to us, everyday tasks such as extracting,rnsearching, understanding and relating relevant data have become intractable. A large percentagernof these information are discussing past, current, and future real world events. Events are dynamicrndata structures that play a key role in understanding phenomena happing in real world, which arernbasically driven by the four â€˜Wâ€™sâ€™ (what, who, when, and where). This natural progression ofrnquestions is a classic example of what one might ask about an event. This results in natural way tornexplain complicated relations between people, places, actions and objects. Event centeredrnmodeling captures the dynamic aspects of an event along with semantic representation of eventrnfacts.rnIn this research work, we have proposed and developed event extraction and representation modelrnfrom Amharic news article. Event modeling involves key event identification, event elementsrnextraction, and event semantic elements representation. Event triggers tells the action taking placernin news article. To identify the mention of event in news article we used manually collected eventrntrigger words and phrases from various news domains. For event elements extraction, we usedrnnamed entity recognizer and other local features like potential trigger, event extent, path from thernextent to head word of the trigger. Machine learning Maximum entropy classifier is trained usingrnevent related news article collected from Fana Broadcast Corporate news archive. For eventrnrepresentation, we designed ontology based event representation model that provides deeperrnsemantic through event information representation.rnA prototype showing an event extraction and representation is developed using differentrnprogramming environment. Evaluation of trigger identification and event elements extraction isrncarried out by comparing manually tagged news article with the automatically extracted eventrninformation by the system. The evaluation result shows that the trigger identifier module obtainrnprecision (67.1%) of event correctly which contributes to the better event elements extraction. Thernevent elements extractor component shows greater obtaining precision (69.1%) while eventrnclassification module classify about (72%) of event correctly. The representative ability of ourrnevent representation model is evaluated with respect to requirements and event dimensions werncovered in this work.