隨著網際網路的蓬勃發展，以及個人網頁編寫的方便性，研究學者的研究著述紀錄例如:發表過的學術論文及書籍等均可從學者的個人著述列表網頁(publication list page)中獲得。藉由分析學者的研究著述紀錄可延伸出許多應用，例如學術社群分析，利益衝突迴避等。然而大部分的個人著述列表網頁是由研究者自行編排設計，因此不同研究學者的個人著述列表網頁均不盡相同，所以如何有效率且正確地從個人著述列表網頁中擷取有用的資訊供分析使用仍然是個深具挑戰性的研究課題。
在此篇論文中，我們提出了一個自動化引用文獻紀錄(citation record) 擷取系統，能自動的從不同樣式的網頁中擷取引用文獻紀錄資訊，而不需要人力介入。我們根據對個人著述列表網頁編排的觀察，發現大部分的引用文獻紀錄會以規律的編排方式呈現在網頁中，所以我們提出利用聚類器整合技術(cluster ensemble)將網頁內容的特徵呈現在一個連續的特徵字串序列中，再利用重複字串序列搜尋(repeat pattern finding)技術找出序列中重複的片段，而這些重複的片段可能代表著網頁中引用文獻紀錄的編排的樣式，最後我們利用引用文獻的特性從這些可能的樣式挑選出代表引用文獻的樣式。最後藉由實驗的分析結果，證明我們所提出的自動化擷取技術確實能正確的從不同樣式的網頁中擷取引用文獻紀錄。

Today, a huge amount of researchers’ publication list pages are available on the Web, which could be an important resource for many value-added applications, such as citation analysis, conflict of interest, and academic social networks. How to gather citation records from those publication list pages efficiently and accurately is still a challenging problem because many of those pages are crafted manually by researchers themselves, and the layouts of those pages and the formats of citation records could be quite different depending on the researchers’ affinities.
In this thesis, we propose a Citation Record Extractor (CRE) system, which is capable of extracting citation records presented with various layouts of publication list pages correctly and automatically. Our cues to solve this problem are inspired from the regular nature of citation records in a given publication list page. A cluster ensemble framework is adopted to analyze the implicitly features of pages and a repeat pattern finding technique is utilized to reveal possible patterns that may represent citation records. Extensive experiments are conducted to measure the effects of all parameters and system performance. The experiment results clearly show that our approach can perform stable and well (with 88.9% of F-measure on average) and outperform the MDR [33] system and two Naïve systems that based on DOM tree structure and Visual separating assumption. We also provide an analysis on our dataset to show that the richness of publication information contained in publication list pages.

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