一個專家搜尋系統即是依照使用者的問題查找出可以解決使用者問題的專家名冊。但傳統的作法主要是依照各個專家的專業能力做人工建檔，使用者再依據資料庫所提供之分類項尋找適合自己問題的專家。或是依照各個專家在相關文獻主題中出現的次數多寡來決定與使用者問題的關聯程度。如此不但將耗費大量人力並且也無法完整表達各個專家對於各主題的熟悉程度。
此系統利用各個專家的著作建立該名專家專業能力資料，並且利用維基百科中的分類鏈結關係計算各個專家與計畫書領域的親疏遠近。在系統評估部分，論文使用了國家科學委員會資訊學門二在2007年所收到之668篇專案計畫書以及853位專家學者候選人為實驗資料集，實驗結果顯示，系統可將資料集中的八項分類計畫書分配給符合專長的專家們，並且在MRR3指標中的專家排序方式也達到良好的成效。

An expert finding system is a system which provides the expert list based on the user’s queries. Traditionally, this process is solved by looking up the expert-expertise databases and ranking by the experts based on the appearance of his/her name in the related documents. However, this causes the process to a significant time-consuming work and database maintaining problems.
This thesis focuses on an expert search scenario in a science organization. A science organization receives hundred of proposals per year. Traditionally, science organization distributes these proposals for reviewers manually and manpower consuming is a critical problem. For these problems, this thesis proposes an expert finding system to build the expertise-expert profile automatically and to rank the experts by the relatedness between the proposals and experts.
The proposed expert finding system uses the publication of candidate experts to build the expertise-expert profile. The link structure of Wikipedia also is adopted to calculate the relatedness between the proposals and experts. In the system evaluation, this thesis uses the dataset from NSC (http://www.nsc.com.tw) 2007 which contains 668 proposals and 853 candidate experts. The experimental results show that the system could distribute the proposals to the experts with the same expertise domain. The experiments of MRR also show that the ranking of experts achieves good performance.

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