在Web2.0的時代，資訊量持續以倍數的速度成長，造成資訊過載的問題越來越嚴重，這樣的問題使使用者無法適時的得到需要的資訊。過多的搜尋結果除了讓一般的網路資訊收集變得困難之外，商業分析師也因此需要花更多的成本以了解企業相關的重要關係，分析產業或科技的資訊需要經過龐大的資料分析工作才能完成。
為了減輕資訊過載的問題，我們根據關聯擷取（relation extraction）的技術所建立的商業生態關聯網路設計一個查詢方法，以期降低使用者搜尋資料的成本，同時提升資訊搜尋的效果，首先結合語意關聯資料，將網際網路上不同來源的資訊做抽象概念的統整，並透過個人化的技術協助資訊過濾，此外，透過擴散激發理論（spreading activation model）搜尋相關的商業知識。
本研究提出的搜尋方法可讓使用者發現更多的商業關聯資訊，進而協助商業分析師從事探索式搜尋（exploratory search），根據實驗的結果，使用本方法搜尋的使用者，相較於使用單純的關鍵字比對及單純的使用者設定（user profile）比對，可在新聞資料中發掘更多的商業關聯。

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