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  • 學位論文

文字探勘技術強化氣候變遷調適知識管理之研究 -以水資源領域為例

Study on Knowledge Management for Climate Change Adaptation with Text Mining Techniques - A Case study of Water Resources

指導教授 : 童慶斌

摘要


文字探勘 (Text mining) 技術讓人們從大量文字獲得有用資訊的過程省時且省力,也因此其相關研究眾多,且其應用相當廣泛。再來,面對氣候變遷產生之衝擊,為減少氣候風險,需要建立氣候變遷調適調適行動,而在建立的過程中必須整理大量文獻資料,但是鮮少有將文字探勘與氣候變遷調適結合的研究。所以,本研究將利用知識金字塔 (DIKW pyramid) 與文字探勘技術,參考科技部氣候變遷調適科技整合研究計畫 (TaiCCAT) 氣候調適六步驟,強化氣候變遷調識知識管理,並以水資源領域為案例進行探討。本研究選用線上文獻資料庫Scopus作為資料來源,選取標題、摘要或關鍵字中包含“climate change”、”adaptation”、”water resources”的文獻。依此條件下載的篇數共有1788篇,年份從1982年至2017年,其中80%來自期刊,而來自Climatic Change的文獻最多,且年份越新的文獻種類及出版品數量越多。利用文字探勘分析摘要的結果,可歸納出與用水、事件、氣候變遷風險評估、其他領域、水文及解決方法相關的類別,藉此可建立各類別的字詞庫,另外亦可得到不同相似度下的分群結果。利用其產出的字詞庫可輔助執行氣候調適建構的第一步驟「界定問題與設定目標」的次步驟「關鍵議題之界定」,可迅速從關心的類別中得知最常被討論的關鍵字詞,而利用分群結果輔助執行第四步驟「界定與評估調適選項」的次步驟「界定調適選項」,推薦參考文獻群,便於後續歸納整理調適選項。

並列摘要


Text mining becomes popular in recent years. It helps people saving many time and efforts when they mining useful information from huge amount of text. As a result, there are many researches and applications about text mining. Another issue getting people much attention is climate change. To reduce the risk of climate change, it is necessary to develop adaptation action plan, which mining useful information is essential. However, there are few researches applying text mining to climate change study. The pourpose of this study is to strengthen climate change adaptation knowledge management by using DIKW pyramid, text minging and the six-step decision support tool developed by the Taiwan integrated Research Program on Climate Change Adaptation Technology (TaiCCAT). In addition, this study focuses on water resources. The textual data are download from Scopus that is an online abstract and citation database. There are 1788 articles that title, abstract or keywords has climate change, adaptation and water resources. Their publication year are from 1982 to 2017, and about 80% of them published by journals. Morever, the most of articles are from the journal of “Climatic Change”. There are two results after using text mining. First result is term frequency. These terms are classified into sereral categories, including water usage, event, climate change risk assessment, other field, hydrology, and solving strategy. These categories can build keyword databases to help users quickly and precisely know the most popular terms of the group when they need to identify key issues. Another result is the cluster analysis to help user narrow down the number of the articles when they need to identify adaptation options.

參考文獻


1. Cooper, P. (2010). Data, information and knowledge. Anaesthesia & Intensive Care Medicine, 11 (12), 505-506. doi:10.1016/j.mpaic.2010.09.008
2. Cooper, P. (2014). Data, information, knowledge and wisdom. Anaesthesia & Intensive Care Medicine, 15 (1), 44-45. doi:10.1016/j.mpaic.2013.11.009
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6. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval: Cambridge University Press.
7. Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33 (2), 163-180. doi:10.1177/0165551506070706

被引用紀錄


廖宜霈、林峰田(2022)。臺灣氣候變遷調適知識本體(CCAO)之建構與應用都市與計劃49(3),263-283。https://doi.org/10.6128%2fCP.202209_49(3).0001

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