隨著部落格的文章越來多，如何在大量的部落格文章中有效的幫助使用者找到需要的文章就變成一個非常重要的問題。因此，本研究結合部落格搜尋和個人化搜尋的技術，提出針對搜尋部落格文章的個人化部落格搜尋。其中，主要包含了兩種個人化部落格搜尋的方法。首先，延伸一般個人化搜尋的方式針對部落格的環境加以修正，提出了內文個人化部落格搜尋。透過分析和搜尋系統使用者相關的部落格文章內文，讓使用者可以更快的在部落格中找到需要的文章。接著，我們提出創新的社交個人化部落格搜尋，藉由分析使用者和其他部落客之間的社交行為，來達到個人化的目的。最後，在實驗中可以發現，個人化部落格搜尋明顯的提升了部落格搜尋的準確度和滿意度。 / As the quantity of articles increased rapidly, how to help the users to find the blog articles effectively becomes an important issue. Therefore, this study combines the traditional blog search and personalized search techniques, and proposes a way to search blog by personal information. This study mainly contains two methods of personalized blog search. First, the general way of personalized search environment for blogs to be amended, the content based personalized blog search. By analyzing and searching the system-related blog article text of the users, this study can make users quickly figure out the articles they need. We propose a social based personalized blog search, by analyzing the social behavior between users and other bloggers, to achieve personal goals. Finally, the result of the experiments has shown that both ways obviously improved search accuracy and satisfaction.

網路購物興盛、社群網站湧現以及資訊搜尋的方便性，持續性資訊搜尋成為購買決策很重要的影響因素。本研究先蒐集相關文獻與彙整搜尋動機的元素，共包括九種資訊搜尋動機，分別是：探索動機、滿足動機、價值動機、思想動機、產品提供豐富度、產品資訊提供、方便性、角色動機以及社交動機，並將這九種元素歸納為三種資訊搜尋動機，分別是：享樂性、功利性以及社交性，再探討搜尋動機與持續性社群網路資訊搜尋對購買意圖的影響關係。本研究透過網路發放問卷，有效樣本共480份，採用量化分析，並以最小平方法(PLS)進行有效樣本之實證分析。 本研究經因素分析發現，探索動機與滿足動機同屬於一種構面，本研究將此構面命名為：「探索滿足」；產品提供豐富度、產品資訊提供、方便性同屬於一種構面，本研究將此構面命名為：「資訊可得」；角色動機與社交動機同屬於一種構面，本研究將此構面命名為：「社交需求」。此外，在思想動機與價值動機方面，本研究則是分別重新將這兩種構面命名為：「資訊潮流」與「優惠需求」，以更符合理論邏輯與這兩種因素的意涵。 本研究結果顯示：研究分析歸納的五種資訊搜尋動機：探索滿足、資訊潮流、資訊可得、優惠需求、社交需求，對於持續性社群網路資訊搜尋皆具有正向的影響；持續性社群網路資訊搜尋對購買意圖亦具有正向的影響關係，而產品需求強度對於持續性社群網路資訊搜尋與購買意圖之間的關係則是不具有調節效果。這代表無論消費者對於產品是否有需求，只要平時在社群網站上瀏覽到產品的相關資訊時，就能影響人們購買意圖的產生，這間接證實了非計劃購買發生的可能，也突顯了持續性資訊搜尋的重要性。最後，本研究結果期望可以提供個人或業者於社群網站上的產品銷售策略之經營與學術未來研究之方向。 / Due to the flourishing of Internet shopping, the emergence of social network sites and the convenience of information search, ongoing search has become a very important purchase decision factors. In this study, we collected relevant literature and aggregated a total of nine kinds of search motivations, namely: adventure, gratification, value, idea, product offerings, product information, convenience, role and social. The nine search motivations are summarized into three search motivations, namely: hedonic, utilitarian and sociality. Then, we explore the effect relationship for the search motivations and ongoing social network information search to the purchase intentions. Then, we did questionnaires via the internet and a total of 480 valid samples were received, after that, we used quantitative analysis and used partial least square (PLS) to conduct empirical analysis. By factor analysis, we found that adventure motivation and gratification motivation belonged to the same factor, we named “exploration meet”. Product offerings, product information and convenience motivation belonged to the same factor, we named “information availability”. Role motivation and social motivation belonged to the same factor, we named “social needs”. In addition, we re-named idea motivation to “information trend” and value motivation to “preferential needs” to make these two kinds of factors be more consistent with logical and theoretical implications. The result of this study showed that the five kinds of search motivations: exploration meet, information trend, information availability, preferential needs and social needs all had positive impacts on ongoing social information search. Ongoing social information also had positive impacts on purchase intentions. But the product needs intensity didn’t have a moderating effect between ongoing social information search and purchase intentions, this result not only indirectly confirmed possibility of unplanned purchase, but also highlighted the importance of ongoing search. Finally, the results of this study could be expected to provide marketing strategy for persons or industries on the social network sites and could be referred for the future of academic research.

本研究旨在了解不同類型的資訊搜尋對購買後的心理狀態的影響。首先，我們探討消費者的產品知識如何影響他們的搜尋量，包括線下購買前搜尋、線上購買前搜尋和持續性搜尋，再來探討這些搜尋行為如何影響消費者的購買後認知失調。本研究共收集了542份有效問卷，我們使用PLS結構方程模型來驗證研究模型。研究結果顯示，消費者產品知識對線下購買前搜尋、線上購買前搜尋和持續性搜尋有正向影響，對購買後認知失調有負向影響。在三種類型的搜尋中，只有線上購買前搜尋對購買後認知失調有負向影響。購買後搜尋對購買後失認知調亦有負向影響。對消費者搜尋行為以及消費者搜尋後心理狀態感興趣的研究者們，期望本研究的發現能為他們未來的研究帶來啟發與幫助。 / This study aimed to understand how different types of information search influences consumers’ post-purchase mental state. First we investigated how consumers’ product knowledge influence their amount of information search, including offline pre-purchase search, online pre-purchase search, and ongoing search. Then we investigate how these searching behaviours influence consumers’ post-purchase dissonance. Total of 542 valid online questionnaires were collected and PLS structural equation model was used to verify our research model. The result showed that consumer product knowledge had positive influence on offline pre-purchase search, online pre-purchase search, ongoing search, and had negative influence toward post-purchase dissonance. Among three types of information search, only online pre-purchase search had negative influence on post-purchase dissonance. Post-purchase search also had negative influence on post-purchase dissonance. For those who are interesting in consumers’ searching behaviours and post-search state of mind, these findings could be referenced in their future research.

Ontology可用來組織、管理與分享知識，Ontology Engineering是一種建構Ontology的過程，建構的過程中，多數的工作需要人費時費力地去完成，因此利用機器來輔助Ontology Engineering成了一門重要的課題。使用Knowledge Discovery的方法協助Ontology Engineering建構Ontology的過程，稱為Ontology Learning，本論文中提出的Ontology Learning方法為分析使用者在搜尋引擎下關鍵字查詢時的行為，加上利用與查詢關鍵字有關的網頁資訊，以輔助建構Ontology。本論文中的Ontology由使用者所查詢的關鍵字組成，我們要learning的，則是這些關鍵字彼此之間的關係，其中有上義詞、下義詞與同義詞等等，因此，自動尋找關鍵字彼此之間的關係以輔助建構Ontology，即為我們提出本論文的目的。除此之外，本論文亦實作了完整的Ontology Learning系統，從一開始使用者查詢記錄的蒐集，關鍵字擷取與分析，關鍵字之間的關係判定，直到最後Ontology的產生，都將由系統自動完成。 / Ontology can be used to organize, manage and share knowledge. Ontology Engineering is the process of constructing Ontology. However, it’s usually a time-consuming and error-prone task. Thus, utilizing methods of Knowledge Discovery to help Ontology Engineering is called Ontology Learning. In this thesis, Ontology Learning process is done by using those pages related query terms and analyzing the querying behavior of users on search engines. The Ontology is organized by user query terms and relations among them. These relations we define are hyperonomy, hyponomy, synonymy and et al. Our goal of this thesis is to automatically learn the correct relations among these query terms. Besides, we implemented the complete system platform for Ontology Learning. The system can automatically collect logs, extract and analyze query keywords, and produce the final Ontology.

網際網路的蓬勃發展帶動了電子商務的興起以及後續而來的許多技術。在電子商務中，企業希望能夠利用網路的特性加速商業流程、降低交易成本，換句話說，企業希望能夠做到資訊系統的動態整合。ebXML是一以此為目標的發展計畫。其所用的技術包括了SOAP、UDDI、WSDL和XML等技術。其中的UDDI是一應用於服務搜尋的技術，其目的是幫助企業尋找網路服務。透過UDDI，企業能夠尋找到網路上符合需求的網路服務，之後才能開始進行後續動作。但是目前的UDDI僅提供了最基礎的搜尋指令，和一般使用者的搜尋習慣並不相同。 本研究藉由分析UDDI規格和使用者習慣，嘗試設計出一套能夠幫助使用者在UDDI上更快速、有效地尋找到相關資訊的系統。

搜尋引擎已成為大多數人普遍使用的電子服務項目。Google是全球網路搜尋的龍頭，但是Google在中國、南韓和俄羅斯三個國家未竟全功，而分別由百度、Naver和 Yandex三個搜尋引擎獨占鰲頭，有著相同語言和文化知識的本國搜尋引擎，成為當地使用網路者之首選。Yandex是俄羅斯排行第一搜尋引擎，尤其是使用俄文搜尋時；也是俄羅斯最具創新的公司。本研究藉由分析Yandex本身及進一步比較如百度和Naver的本土搜尋引擎，探討Yandex成為俄羅斯網路搜尋龍頭的最主要因素。 百度、Naver和Yandex這三間公司會成為當地最重要的搜尋引擎，其原因最大的共通性是「高度本地化的服務」。相較於Google這類外國公司，更了解當地市場的需求。以Yandex為例，其擁有突破性的技術，專注於搜集且貼近當地需求，充分發揮在地化的服務，進而保持在市場上的領導地位。 Yandex未來發展將專注於擴大搜尋服務到新興市場，精進其於移動應用服務及因應全球趨勢。身為一個在俄羅斯網路搜尋界的領導者，沒有其他競爭者比Yandex更會培訓旗下的企業者，運用機會，成功地吸引到本地網路使用者。 / Search engines have become usual service for the large majority of people. Google is acknowledged leader in global Internet search. There are three countries where Google has failed to dominate the search market. Baidu, Naver and Yandex are clear market leaders in China, South Korea and Russia respectively, where local language and cultural knowledge has made them first choice for many web users in those markets.Yandex is a Russian based search engine focused on search in Russian language and number one innovative company in Russia.Through business analysis of Yandex itself, and other local search engines Baidu and Naver, and their further comparison, this research discusses the main factors that made Yandex a leader in Russian Internet search. Through the case study the author concluded the factors led to dominance on local markets of each search engines discussed have similarity among three companies Yandex, Naver and Baidu. Their highly localized services brought them comparatively stable and long-term market leadership. All of those companies know local markets better than foreign based competitors such as Google. As for Yandex, breakthrough technology, focus on search and better market understanding and localizing all the services played sufficient role in keeping a leadership position on the market. This study suggested Yandex future development should be focused on expanding into new geographical markets in search industry, as well as will be highlighted with expanding its mobile applications service along with global mobilization trend. As a leader in Russian Internet search, there is nobody better placed than Yandex to educate business practitioners in the Internet on how to tap into those opportunities and successfully target the local audience.

在機會網路上，訊息的遞送遠比一般網路來得困難許多，溝通交換資訊效率很低。本篇論文以山文誌資訊系統為背景，假設在山區中已佈建完成控制節點並組成控制網路，以及行動代理人機制已導入在控制網路上用來搜尋移動的目標節點。其中行動代理人附屬於登山客所攜帶的設備上，欲搜尋的目標節點會沿著登山路徑不斷移動造成搜尋上的困難，若搜尋失敗不只拉長延後了搜尋時間，也可能錯失黃金救難時間造成極大的損失，如何增進搜尋效率是機會網路上相當重要的議題。為此，本文提出一個搜尋方法，在任意的時間點計算目標行動節點落在每個控制節點之間路段的機率，預測目標代理人的位置，就可依機率高低逐次搜尋各路段，以提高搜尋效率。我們以山文誌登山資訊系統，作為參考的機會網路，提出兩個模型，使用機率預測搜尋法，預測行動節點可能所在位置優先搜尋此路段來降低整體搜尋時間，透過一連串的實驗驗證機率模型之準確度，並評估本法之搜尋效率以及當各路段花費時間的機率分佈假設有誤時，搜尋效率的受損程度。在我們的實驗中，機率模型之準確度極高，誤差不超過7.59%，搜尋效率都在44.44以上，即使機率分佈錯誤，搜尋效能仍高於二分搜尋法約2倍。 / Since transmitting data on an opportunistic network is more difficult than that on a general network, information exchanging is less efficient. Based on “CenWits” system, we assume that control point has entirely construed all over the mountains and a control network has completed altogether; meanwhile, the mobile agent mechanism has applied in the searching of mobile target nodes. With mobile agent attached on the equipment of hikers, the target agent moving constantly along the hiking path grows the difficulties in searching. The failure in locating the mobile agent possibly not only prolongs the searching time, but also misses the golden time of life saving, and causes enormously damages eventually. Therefore, figuring that “improving the efficiency of searching” is a major issue in opportunistic network, in this thesis we develop a searching method which enables us to calculate the probability where a mobile target agent locates in every edge between control points in any arbitrary time point. Through forecasting the location of the target agent, we can start searching from the edge with the highest probability, thus enhance the efficiency of searching. Using “CenWits” system as reference opportunistic network, we designed two probability models as well as associated search methods. We conducted a series of experiments to evaluate the accuracy of probabilistic models and the performance of the proposed search methods. In our experiments, the error of probability models is no more than 7.59%. Our proposed methods out perform Basic Binary Search by 44.44 in average. Furthermore, assuming that there is a discrepancy between the probability assumptions and the real distribution of the traveling time spent on each edge, we evaluate the performance degradation too. The experimental results show that under such circumstance, our Probabilistic Prediction Method can even outperform Basic Binary Search by approximately 200%.

所謂的SSE (Symmetric Searchable Encryption;對稱式可搜尋加密)就是將自己的資料用自己的密鑰加密外包放在一個網路或雲端上的資料庫DB，存放期間使用者能保有向DB搜尋資料的能力，而DB仍然可以在不知道資料明文的情況下，把使用者所欲搜尋的資料回傳給他。2013年林峻立等學者針對上述的雲端環境，提出了具動態維護及合取關鍵字搜尋之無連結性密文搜尋機制，此方案利用SSE的方式能讓雲端上的伺服器快速搜尋到所提交的關鍵字相關檔案，但此方案所提出之協議仍有安全上的弱點。因此，在此篇論文中，我們基於此方案，分析其安全性漏洞，並在兼顧安全性與低成本的特性下，提出新的SSE搜尋機制。

在高度競爭的時代，技術交易是廠商取得技術的重要方法之一。隨著智慧財產權法治的規範愈來愈完整，廠商對智慧財產權重要性的體認也逐漸提高，如何在技術市場中搜尋適當的技術交易對象，是許多廠商感到困擾的問題，而其搜尋過程也是技術交易研究領域中較為不足之處。因此本研究擬針對不同的技術交易情境下，如何成功地搜尋到交易的對象來進行研究。 技術的交易可以視為是知識商品的交易，目前技術搜尋的研究多以策略決策觀點或組織學習的觀點來加以著墨，其結果大多與廠商行為理論相關，對於技術交易搜尋過程的探討仍屬有限。事實上，技術搜尋是買賣雙方與訊息提供者彼此在搜尋過程中的互動行為，主要建立在人與人彼此連接的網絡上。因此，技術知識或資訊在其交易搜尋網絡上的流動可視為一種複雜系統，影響的因素較為複雜。一般而言，傳統的研究方法通常較不易觀察到多個變項對整體研究的影響，若用以探討技術交易的複雜過程，可能無法完整呈現技術交易搜尋過程的全貌；再者，傳統的研究方法大多依據過去專利、研發投資等數據來進行探討技術搜尋的研究，其研究指標對技術搜尋的適用性也值得考量。 本研究以複雜調適系統為理論基礎，依據相關理論與個案訪談的結果，來建置電腦模擬系統。模擬模型中包括：自變項—買賣方的搜尋行為，包括有知識累加速率與隨機搜尋兩種，以及與系統環境有關的系統規模、買賣家數數量、技術知識權重等。就交易與否的部分以個案資料假設的交易成功機率加以處理；應變項—特徵路徑長度（交易成功所經過的總agent數）及每個agent被經過的次數，據以在各種設定條件下的系統情境來進行模擬分析。 本研究結果發現，技術交易系統確實表現出複雜調適理論的特性，例如：非線性、聚集、流動、多樣性等特徵；交易搜尋效率同時受到知識累加速率與隨機搜尋兩種策略行為的影響，搜尋最適條件一般出現在知識累加速率和搜尋隨機機率均為中間值時，其中路徑相依度對交易搜尋效率的影響較知識內隱性明顯。網絡結構的影響，可以從密集性、連結關係與集散性三個面向來描述，搜尋行為影響網絡節結構的形成，知識累加速率高時產生的結構較密集，反之，隨機搜尋的行為對應較分散的結構，而均衡型結構的搜尋效率較佳。總之，本研究結果顯示，於技術交易進行的實務上，若能同時兼顧既有的知識累加速率與擴大原有的路徑，將有利於交易搜尋效率的提升。

本篇論文建立一個藉由直覺性素描搜尋模型的系統，結合筆觸繪圖搜尋手繪圖與模型。希望可以藉由本系統，提供使用者比起關鍵字或模型搜尋模型，更加方便的模型搜尋工具。系統主要分為建立索引檔、比對特徵向量和使用者介面三個部分。建立索引檔部分要將三維模型處理成資料庫可認知的資料型態，首先將模型旋轉到不同角度並且將之從三維空間描繪成二維模型投影圖，再透過分類演算法把模型投影圖和手繪圖描述為二維特徵向量。比對特徵向量部分需建立手繪圖資料庫和三維模型資料庫的橋梁，藉由計算兩者的特徵向量之間的距離與角度，得到相似度的排序。使用者介面部分提供直覺性使用者繪畫的介面，以不影響使用者創造性的前提下，在使用者繪畫過程中給予最相似於使用者繪畫的手繪圖結果，使用者可以藉由臨摹此結果更貼近所想繪畫的物體，更進一步地取得模型的搜尋結果。最後我們將透過統計方法去驗證系統的有效性。 / We proposed an intuitive model retrieval system with a sketch interface for a database contains sketch drawings and 3D models. Benefit the sketch interface, the proposed system can facilitate the search process better than keyword query or search by 3D model. The system begins with offline indexing preprocess which convert the 3D models into feature vectors. Under best view selection, we render each 3D model into a 2D feature line image. Then classification method will apply the line images and sketching images in model database to build the feature vector. The rank of matching is computed with the angle between the feature vector of input sketch image and feature line images in the database. To extend the usability, we design a sketch interface for searching the best match result during the drawing process. For suggesting the drawing hint, candidate matching results are listed aside to the sketch input screen. We use statistical method to evaluate the feasibility of the proposed system.