對於人們來說，新聞是了解世界各地大小事情所不可或缺的資源，而網路新聞在近幾年更是變得越來越熱門。雖然現在檯面上已有相當多的新聞應用服務，但是僅有少數可以讓使用者有效率地瞭解新聞內容。為了解決這個問題，我們首先提出Syntax Rule-based Event Processing Model (SREPM)來擷取新聞文章中以(主詞, 動詞, 受詞)模式出現的事件。接著，在廣義知網的知識支援下，我們定義了一個結合「人、事、時、地、物」實體與事件鍊，並且以後者為中心的知識圖架構。最後，基於事件知識圖以及廣義知網裡的知識，我們推出了一個新聞對話服務機器人，它不僅可以提供視覺化後的事件知識圖以供讀者快速了解新聞概要，更能在五合學的基礎上，回應讀者們相關的問題。

News is an essential source for people to understand recent events around the world, and online news is becoming more and more popular in these years. Despite various services and apps specializing in news, few of them can fit the needs of understanding news stories efficiently. In order to solve this problem, we first proposed a Syntax Rule-based Event Processing Model (SREPM) that can extract events in articles on the basis of (Subject, Verb, Object) pattern. Second, with the supporting knowledge from E-HowNet, we defined an event-centric knowledge graph which combines the recognized entities of Time, Places, Items, Roles and Events with event-chains from SREPM, making visualization of it with the purpose of offering quick outline for user. Last but not least, we proposed News Bot – a conversational news service system based on the event-centric knowledge graph, aiming to answer questions about news articles regarding to Five W’s in journalism.

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