對於學者所發表的著作，通常會以引用文獻 (Citation) 的格式來紀錄。此外，為了方便查詢與統計分析，書目數位圖書館 (Bibliographic Digital Library) 會將引用文獻分類索引。然而，我們發現同名作者的引用文獻無法正確地被書目數位圖書館依照作者身分索引。造成錯誤作者索引的問題有兩個，分別是資訊量不足的問題與資訊混淆的問題。這兩個問題使得同一作者的引用文獻會被辨識為不同作者的，不同作者的引用文獻卻會被認為是同一作者的。

在此篇論文中，我們提出了一種新的作者身分消歧方法用以解決上述的兩個問題。此方法可藉由搜尋網路文件來擴充引用文獻的資訊量，並利用此資訊來幫助引用文獻的作者身分消歧。此外，一種基於學習配對的聚類方法被應用以將同一作者的引用文獻聚為一類，並且使用提出的配對過濾器來減輕高度混淆資訊影響消歧結果。實驗結果證實，我們提出的方法可以有效改善因資訊量不足所造成錯誤消歧結果，且消歧結果的品質也可藉由使用提出的配對過濾器而提升。

In general, the publications of scholars are often recorded by using the citation format. Moreover, bibliographic digital libraries would index those citations in order to provide a search service which is convenient to users and some statistics analysis. However, we observe that the bibliographic digital libraries cannot index the citations with the same author name by authorship correctly. There are two reasons for the wrong author indices, i.e. information shortage problem and information ambiguity problem. Due to the two problems, the citations authored by the same individual would be identified as different individuals’ ones, and citations of different individuals would be identified as the same individual’s ones.

In this thesis, we proposed a novel authorship disambiguation approach to solve the information shortage problem and the information ambiguity problem. This approach is based on web documents searching to enrich the citation information, and this kind of web information is used for helping authorship disambiguation in citations. Besides, the cluster algorithm based on a learned pairing function is applied to cluster the citations authored by the same individual together, and a proposed pair filter is used for alleviating the influence of high ambiguous information on authorship disambiguation. The experimental results show that the incorrect disambiguation result caused by the information shortage problem can be improved by using the approach we proposed, and the performance of authorship disambiguation can be improved again when the proposed pair filter is used.

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