在一個給定的主題下，去搜尋有關該主題的專家在許多現實世界的情況中是一個非常迫切的問題，例如:尋找共同合作對象或是找一個有專業人士來解決特殊的問題。雖然如此，過去的研究只著重在一個專家候選人的名字出現在相關主題文件的次數來決定該候選人是否具有符合給定主題的專長，如此一來搜尋到的專家可能不是最適合的，這是因為無法確認該候選人在給定主題下是否具有可靠的權威性，因此為了解決此問題，我們提出一個專家搜尋系統稱做“權威專家搜尋系統”。
本系統是依據專家們的出版作品的品質及權威性來判定專家們的專長。本系統利用非文字訊息，也就是影響力指數(impact factor)來決定出版作品的品質並且提出一個基於社會網路分析的概念的參考文獻辨別的方法來移除掉重複的鍵值。在我們的實驗過程中，我們比較相關的方法顯示: (1) 我們提出的方法在正確率和精確率的調和平均數(F-measure)達到良好的效率; (2) 在參考文獻辨別的方法中，可以減少訓練例子的數目，(3) 非文字訊息對搜尋專家是有用的。

Searching for experts on a given topic is a critical problem in many real-world situations, such as collaborative finding or speaker finding. Even so, previous works have only focused on searching for experts based on the appearance of topic query in an organization’s documents, which means that the experts selected might not be suitable for the task at hand.

In order to resolve this problem, we propose an Authoritative Expert Finding System, called AEFS, which ranks the publications of experts to indicate their level of expertise. AEFS uses non-textual information, e.g. impact factor, to represent the quality of publications, and provides a citation matching function that removes duplicated citations based on the concept of centrality in social network analysis (SNA).In our experiments, we compare a number of related approaches to show that: (1) the proposed approach achieves a good performance in terms of the average F-measure;(2) citation matching can reduce the number of training examples required; and (3)impact factor are very effective for searching experts.

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