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題名 資訊檢索之學術智慧
Research Intelligence Involving Information Retrieval作者 杜逸寧
Tu, Yi-Ning貢獻者 諶家蘭<br>林我聰
Seng, Jia-Lang<br>Lin, Woo-Tsong
杜逸寧
Tu, Yi-Ning關鍵詞 議題的發現與追蹤
資料探勘
資訊檢索
學術智慧
貝氏估計
新穎度指標
發表量指標
引文分析
Topic discovery and tracking
data mining
information retrieval
Bayesian estimation
academic intelligence
novelty index
published volume index
citation analysis日期 2009 上傳時間 4-Sep-2013 16:55:05 (UTC+8) 摘要 偵測新興議題對於研究者而言是一個相當重要的問題，研究者如何在有限的時間和資源下探討同一領域內的新興議題將比解決已經成熟的議題帶來較大的貢獻和影響力。本研究將致力於協助研究者偵測新興且具有未來潛力的研究議題，並且從學術論文中探究對於研究者在做研究中有幫助的學術智慧。在搜尋可能具有研究潛力的議題時，我們假設具有研究潛力的議題將會由同一領域中較具有影響力的作者和刊物發表出，因此本研究使用貝式估計的方法去推估同一領域中相關的研究者和學術刊物對於該領域的影響力，進而藉由這些資訊可以找出未來具有潛力的新興候選議題。此外就我們所知的議題偵測文獻中對於認定一個議題是否已經趨於成熟或者是否新穎且具有研究的潛力仍然缺乏有效及普遍使用的衡量工具，因此本研究試圖去發展有效的衡量工具以評估議題就本身的發展生命週期是否仍然具有繼續投入的學術價值。 本研究從許多重要的資料庫中挑選了和資料探勘和資訊檢索相關的論文並且驗證這些在會議論文中所涵蓋的議題將會領導後續幾年期刊論文相似的議題。此外本研究也使用了一些已經存在的演算法並且結合這些演算法發展一個檢測的流程幫助研究者去偵測學術論文中的領導趨勢並發掘學術智慧。本研究使用貝式估計的方法試圖從已經發表的資訊和被引用的資訊來建構估計作者和刊物的影響力的事前機率與概似函數，並且計算出同一領域重要的作者和刊物的影響力，當這些作者和刊物的論文發表時將會相對的具有被觀察的價值，進而檢定這些新興候選議題是否會成為新興議題。而找出的重要研究議題雖然已經縮小探索的範圍，但是仍然有可能是發展成熟的議題使得具有影響力的作者和刊物都必須討論，因此需要評估議題未來潛力的指標或工具。然而目前文獻中對於評估議題成熟的方法僅著重在議題的出現頻率而忽視了議題的新穎度也是重要的指標，另一方面也有只為了找出新議題並沒有顧及這個議題是否具有未來的潛力。更重要的是單一的使用出現頻率的曲線只能在議題已經成熟之後才能確定這是一個重要的議題，使得這種方法成為落後的指標。 本研究試圖提出解決這些困境的指標進而發展成衡量新興議題潛力的方法。這些指標包含了新穎度指標、發表量指標和偵測點指標，藉由這些指標和曲線可以在新興議題的偵測中提供更多前導性的資訊幫助研究者去建構各自領域中新興議題的偵測標準。偵測點所代表的意義並非這個議題開始新興的正確日期，它代表了這個議題在自己發展的生命週期上最具有研究的潛力和價值的時間點，因此偵測點會根據後來的蓬勃發展而在時間上產生遞延的結果，這表示我們的指標可以偵測出議題生命力的延續。相對於傳統的次數分配曲線可以看出議題的崛起和衰退，本研究的發表量指標更能以生命週期的概念去看出議題在各個時間點的發展潛力。本研究希望從這些過程中所發現的學術智慧可以幫助研究者建構各自領域的議題偵測標準，節省大量人力與時間於探究新興議題。本研究所提出的新方法不僅可以解決影響因子這個指標的缺點，此外還可以使用作者和刊物的影響力去針對一個尚未累積任何索引次數的論文進行潛力偵測，解決Google 學術搜尋目前總是在論文已經被很多檢索之後才能確定論文重要性的缺點，學者總是希望能夠領先發現重要的議題或論文。然而，我們以議題為導向的檢索方法相信可以更確實的滿足研究者在搜尋議題或論文上的需求。
This research presents endeavors that seek to identify the emerging topics for researchers and pinpoint research intelligence via academic papers. It is intended to reveal the connection between topics investigated by conference papers and journal papers which can help the research decrease the plenty of time and effort to detect all the academic papers. In order to detect the emerging research topics the study uses the Bayesian estimation approach to estimate the impact of the authors and publications may have on a topic and to discover candidate emerging topics by the combination of the impact authors and publications. Finally the research also develops the measurement tools which could assess the research potential of these topics to find the emerging topics. This research selected huge of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study also uses some existing algorithms and combination of these algorithms to propose a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals. The research uses the Bayesian estimation approach and citation analysis methods to construct the prior distribution and likelihood function of the authors and publications in a topic. Because the topics published by these authors and publications will get more attention and valuable than others. Researchers can assess the potential of these candidate emerging topics. Although the topics we recommend decrease the range of the searching space, these topics may so popular that even all of the impact authors and publications discuss it. The measurement tools or indices are need. But the current methods only focus on the frequency of subjects, and ignore the novelty of subjects which is critical and beyond the frequency study or only focus one of them and without considering the potential of the topics. Some of them only use the curve of published frequency will make the index as a backward one. This research tackles the inadequacy to propose a set of new indices of novelty for emerging topic detection. They are the novelty index (NI) and the published volume index (PVI). These indices are then utilized to determine the detection point (DP) of emerging topics. The detection point (DP) is not the real time which the topic starts to be emerging, but it represents the topic have the highest potential no matter in novelty or hotness for research in its life cycle. Different from the absolute frequent method which can really find the exact emerging period of the topic, the PVI uses the accumulative relative frequency and tries to detect the research potential timing of its life cycle. Following the detection points, the intersection decides the worthiness of a new topic. Readers following the algorithms presented this thesis will be able to decide the novelty and life span of an emerging topic in their field. The novel methods we proposed can improve the limitations of impact factor proposed by ISI. Besides, it uses the impact power of the authors and the publication in a topic to measure the impact power of a paper before it really has been an impact paper can solve the limitations of Google scholar’s approach. 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Learning from multi-topic web documents for contextual advertisement, In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1051-1059. 描述 博士
國立政治大學
資訊管理研究所
94356509
98資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094356509 資料類型 thesis dc.contributor.advisor 諶家蘭<br>林我聰 zh_TW dc.contributor.advisor Seng, Jia-Lang<br>Lin, Woo-Tsong en_US dc.contributor.author (Authors) 杜逸寧 zh_TW dc.contributor.author (Authors) Tu, Yi-Ning en_US dc.creator (作者) 杜逸寧 zh_TW dc.creator (作者) Tu, Yi-Ning en_US dc.date (日期) 2009 en_US dc.date.accessioned 4-Sep-2013 16:55:05 (UTC+8) - dc.date.available 4-Sep-2013 16:55:05 (UTC+8) - dc.date.issued (上傳時間) 4-Sep-2013 16:55:05 (UTC+8) - dc.identifier (Other Identifiers) G0094356509 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60196 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理研究所 zh_TW dc.description (描述) 94356509 zh_TW dc.description (描述) 98 zh_TW dc.description.abstract (摘要) 偵測新興議題對於研究者而言是一個相當重要的問題，研究者如何在有限的時間和資源下探討同一領域內的新興議題將比解決已經成熟的議題帶來較大的貢獻和影響力。本研究將致力於協助研究者偵測新興且具有未來潛力的研究議題，並且從學術論文中探究對於研究者在做研究中有幫助的學術智慧。在搜尋可能具有研究潛力的議題時，我們假設具有研究潛力的議題將會由同一領域中較具有影響力的作者和刊物發表出，因此本研究使用貝式估計的方法去推估同一領域中相關的研究者和學術刊物對於該領域的影響力，進而藉由這些資訊可以找出未來具有潛力的新興候選議題。此外就我們所知的議題偵測文獻中對於認定一個議題是否已經趨於成熟或者是否新穎且具有研究的潛力仍然缺乏有效及普遍使用的衡量工具，因此本研究試圖去發展有效的衡量工具以評估議題就本身的發展生命週期是否仍然具有繼續投入的學術價值。 本研究從許多重要的資料庫中挑選了和資料探勘和資訊檢索相關的論文並且驗證這些在會議論文中所涵蓋的議題將會領導後續幾年期刊論文相似的議題。此外本研究也使用了一些已經存在的演算法並且結合這些演算法發展一個檢測的流程幫助研究者去偵測學術論文中的領導趨勢並發掘學術智慧。本研究使用貝式估計的方法試圖從已經發表的資訊和被引用的資訊來建構估計作者和刊物的影響力的事前機率與概似函數，並且計算出同一領域重要的作者和刊物的影響力，當這些作者和刊物的論文發表時將會相對的具有被觀察的價值，進而檢定這些新興候選議題是否會成為新興議題。而找出的重要研究議題雖然已經縮小探索的範圍，但是仍然有可能是發展成熟的議題使得具有影響力的作者和刊物都必須討論，因此需要評估議題未來潛力的指標或工具。然而目前文獻中對於評估議題成熟的方法僅著重在議題的出現頻率而忽視了議題的新穎度也是重要的指標，另一方面也有只為了找出新議題並沒有顧及這個議題是否具有未來的潛力。更重要的是單一的使用出現頻率的曲線只能在議題已經成熟之後才能確定這是一個重要的議題，使得這種方法成為落後的指標。 本研究試圖提出解決這些困境的指標進而發展成衡量新興議題潛力的方法。這些指標包含了新穎度指標、發表量指標和偵測點指標，藉由這些指標和曲線可以在新興議題的偵測中提供更多前導性的資訊幫助研究者去建構各自領域中新興議題的偵測標準。偵測點所代表的意義並非這個議題開始新興的正確日期，它代表了這個議題在自己發展的生命週期上最具有研究的潛力和價值的時間點，因此偵測點會根據後來的蓬勃發展而在時間上產生遞延的結果，這表示我們的指標可以偵測出議題生命力的延續。相對於傳統的次數分配曲線可以看出議題的崛起和衰退，本研究的發表量指標更能以生命週期的概念去看出議題在各個時間點的發展潛力。本研究希望從這些過程中所發現的學術智慧可以幫助研究者建構各自領域的議題偵測標準，節省大量人力與時間於探究新興議題。本研究所提出的新方法不僅可以解決影響因子這個指標的缺點，此外還可以使用作者和刊物的影響力去針對一個尚未累積任何索引次數的論文進行潛力偵測，解決Google 學術搜尋目前總是在論文已經被很多檢索之後才能確定論文重要性的缺點，學者總是希望能夠領先發現重要的議題或論文。然而，我們以議題為導向的檢索方法相信可以更確實的滿足研究者在搜尋議題或論文上的需求。 zh_TW dc.description.abstract (摘要) This research presents endeavors that seek to identify the emerging topics for researchers and pinpoint research intelligence via academic papers. It is intended to reveal the connection between topics investigated by conference papers and journal papers which can help the research decrease the plenty of time and effort to detect all the academic papers. In order to detect the emerging research topics the study uses the Bayesian estimation approach to estimate the impact of the authors and publications may have on a topic and to discover candidate emerging topics by the combination of the impact authors and publications. Finally the research also develops the measurement tools which could assess the research potential of these topics to find the emerging topics. This research selected huge of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study also uses some existing algorithms and combination of these algorithms to propose a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals. The research uses the Bayesian estimation approach and citation analysis methods to construct the prior distribution and likelihood function of the authors and publications in a topic. Because the topics published by these authors and publications will get more attention and valuable than others. Researchers can assess the potential of these candidate emerging topics. Although the topics we recommend decrease the range of the searching space, these topics may so popular that even all of the impact authors and publications discuss it. The measurement tools or indices are need. But the current methods only focus on the frequency of subjects, and ignore the novelty of subjects which is critical and beyond the frequency study or only focus one of them and without considering the potential of the topics. Some of them only use the curve of published frequency will make the index as a backward one. This research tackles the inadequacy to propose a set of new indices of novelty for emerging topic detection. They are the novelty index (NI) and the published volume index (PVI). These indices are then utilized to determine the detection point (DP) of emerging topics. The detection point (DP) is not the real time which the topic starts to be emerging, but it represents the topic have the highest potential no matter in novelty or hotness for research in its life cycle. Different from the absolute frequent method which can really find the exact emerging period of the topic, the PVI uses the accumulative relative frequency and tries to detect the research potential timing of its life cycle. Following the detection points, the intersection decides the worthiness of a new topic. Readers following the algorithms presented this thesis will be able to decide the novelty and life span of an emerging topic in their field. The novel methods we proposed can improve the limitations of impact factor proposed by ISI. Besides, it uses the impact power of the authors and the publication in a topic to measure the impact power of a paper before it really has been an impact paper can solve the limitations of Google scholar’s approach. We suggest that the topic oriented thinking of our methods can really help the researchers to solve their problems of searching the valuable topics. en_US dc.description.tableofcontents Chapter 1 Introduction 11.1 Research Background 31.2 Research Issue 51.2.1 Research Intelligence between Conferences and Journals 51.2.2 Detecting Candidate Emerging Research Topics via the Bayesian Estimation of Author-Publication Correlations. 101.2.3 Developing the Emerging Topic Detection Indices 111.3 Thesis Organization 12Chapter 2 Literature Review 132.1 Topic Detection and Tracking 132.2 Emerging Topic Detection 152.3 Aging Theory 162.4 Information Retrieval Approach 172.5 Summary 20Chapter 3 The Leading Relationship between Conferences and Journals 223.1 Experimental Design 223.2 Data Selection 253.2.1 Select the Domain 253.2.2 Use the Keywords to Represent the Domain 253.2.3 Choose Databases and Search Engines 263.2.4 Pick the Descriptor of the Paper 283.3 Datasets Properties 293.3.1 Search Conference Papers 293.3.2 Search Journal Papers 313.4 Application of Information Retrieval 323.4.1 Identify Each Document 323.4.2 Calculate Frequency of Appearances 333.4.3 Summarize the Frequency of Conference Papers and Journal Papers 333.4.4 Compute Similarity between Conference Papers and Journal Papers 343.5 Relationship between Conference Papers and Journal Papers 35Chapter 4 The Experimental Results of the Relationship between Conferences and Journals 374.1 Experimental Results 374.2 Discussion 404.3 Summery 43Chapter 5 The Detection of Impact Research Topics via Bayesian Estimation of Author-Publication Correlations 455.1 The Idea of Detecting Impact Research Topics 465.2 Measuring an Author’s Impact Power 485.2.1 Prior Impact Power of an Author 485.2.2 Likelihood Function of the Impact Power of an Author 495.2.3 Posterior Impact Power of an Author 505.3 Measuring the Impact Power of a Publication 515.3.1 Prior Impact Power of a Publication 525.3.2 Likelihood Function of the Impact Power of a Publication 525.3.3 Posterior Impact Power of a Publication 535.4 Measuring the Impact Power of a Paper and a Topic 545.4.1 Impact Power of a Paper 555.4.2 Impact Power of a Topic 57Chapter 6 Determination of Impact Research Topics via the Bayesian Estimation of Author-Publication Correlations 596.1 Experiment to Validate an Author’s Impact Power 596.1.1 Comparing the author’s impact power with previous work 596.1.2 Comparing the impact power of authors with the expert survey 636.2 Experiment to Validate the Impact Power of Publications 656.2.1 Comparing the impact power of publications using the impact factor 666.2.2 Comparing the impact power of publications using the publication list of authors recommended in previous work 706.2.3 Comparing the impact power of publications with the experts survey 736.3 How to find impact research topics using the proposed model 77Chapter 7 The Indices for Emerging Topic Detection 827.1 Novelty of Emerging Topics 827.1.1 Term, Candidate Research Topic, Research Topic, Hot Topic and Emerging Topic 827.1.2 Novelty Index 837.1.3 Published Volume Index 857.1.4 Detection Point 877.2 Information Produced by the Emerging Topic Detection Indices 897.2.1 Year of the Detection Point 897.2.2 The Detection Point Value 897.3 The Properties of Emerging Topic Detection Indices 907.3.1 Novelty Index Properties 907.3.2 Published Volume Index Properties 917.3.3 Detection Point Properties 927.4 The Emerging Topic Detection Table 93Chapter 8 The Research Experiment of the Development of Emerging Topic Detection Indices 958.1 Experimental Design 958.1.1 Choose the Field and Data Resource 958.1.2 Select the Descriptor 968.1.3 Investigate the Extracted Topics 968.2 Experimental Results 978.3 How to Use the Emerging Topic Detection Table to Predict Whether a Topic Warrant Further Research 998.4 Validate the Accuracy and Effectiveness of the Emerging Topic Detection Indices 1018.5 Discussion 1078.6 Summary 109Chapter 9 Conclusions and Future Work 1119.1 Conclusions 1119.2 Future Work 113References 115Appendix A Questionnaire 121 zh_TW dc.format.extent 1964480 bytes - dc.format.extent 1964480 bytes - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094356509 en_US dc.subject (關鍵詞) 議題的發現與追蹤 zh_TW dc.subject (關鍵詞) 資料探勘 zh_TW dc.subject (關鍵詞) 資訊檢索 zh_TW dc.subject (關鍵詞) 學術智慧 zh_TW dc.subject (關鍵詞) 貝氏估計 zh_TW dc.subject (關鍵詞) 新穎度指標 zh_TW dc.subject (關鍵詞) 發表量指標 zh_TW dc.subject (關鍵詞) 引文分析 zh_TW dc.subject (關鍵詞) Topic discovery and tracking en_US dc.subject (關鍵詞) data mining en_US dc.subject (關鍵詞) information retrieval en_US dc.subject (關鍵詞) Bayesian estimation en_US dc.subject (關鍵詞) academic intelligence en_US dc.subject (關鍵詞) novelty index en_US dc.subject (關鍵詞) published volume index en_US dc.subject (關鍵詞) citation analysis en_US dc.title (題名) 資訊檢索之學術智慧 zh_TW dc.title (題名) Research Intelligence Involving Information Retrieval en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) Allan, J., Carbonell, J., Doddington, G., Yamron, J., & Yang, T. 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