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In today's significant and rapidly increasing amount of scientific publications, exploring recent studies in a given research area and building an effective scientific collaboration has become more challenging than any time before. Scientific production growth has been increasing the difficulties for identifying the most relevant papers to cite or to find an appropriate conference or journal to submit a paper to publish. As a result, authors and publishers rely on different analytical approaches in order to measure the relationship among the citation network. Different parameters have been used such as the impact factor, number of citations, co-citation to assess the impact of the produced research publication. However, using one assessing factor considers only one level of relationship exploration, since it does not reflect the effect of the other factors. In this thesis, we propose an approach to measure the Academic Citation Impact that will help to identify the impact of articles, authors, and venues at their extended nearby citation network. We combine the content similarity with the bibliometric indices to evaluate the citation impact of articles, authors, and venues in their surrounding citation network. Using the article metadata, we calculate the semantic similarity between any two articles in the extended network. Then we use the similarity score and bibliometric indices to evaluate the impact of the articles, authors, and venues among their extended nearby citation network. Furthermore, we propose an academic recommendation model to identify the latent preferences among the citation network of the given article in order to expose the concealed connection between the academic objects (articles, authors, and venues) at the citation network of the given article. To reveal the degree of trust for collaboration between academic objects (articles, authors, and venues), we use the similarity learning to estimate the collaborative confidence score that represents the anticipation of a prospect relationship between the academic objects among a scientific community. We conducted an offline experiment to measure the accuracy of delivering personalized recommendations, based on the user’s selection preferences; real-world datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to baseline recommendation algorithms that consider co-citation information.

每一項學術研究進行，其理論基礎都必需要建立於過去已完成的研究之上，因此文獻尋找與探討是進行研究過程非常重要的一個步驟。在數位時代與網際網路的加乘效益之下，改變了過去研究者必需為參考文獻東奔西跑的文獻資料尋找方式，但是卻會造成研究者被許多數位文獻淹沒。借用自網頁分析技術而設計的Google學術搜尋網路工具，能透過已經計算好的文獻權重PaperRank排序使用者所尋找的文獻集合，讓使用者能在數位文獻之中依單篇文獻被引用次數為原則而理出頭緒，但其順序式的排列仍然不能夠揭露出搜尋到的文獻集合裡彼此之間的關聯，其中包括了文獻所使用的關鍵字、作者與參考文獻。為了處理了解文獻中多維度的複雜資料關聯，最好的方式還是依賴人類的視覺化資訊處理能力，特別是當資料量大並且需要在短時間內決策時。 此外使用在文獻分析研究中，學者們使用共同引用(co-citation)、共同作者(co-work)、共同作者引用(co-author)等分析方式，配合延伸自社會網路分析理論中的社會密度(social distance)、關聯層級(social degree)、群(clique)等參數概念，試將複雜的文獻資料有脈絡地按排供參考。僅管此是工作難以機械化且消耗時間的(Börner, Chen , Boyack, 2003)，但是卻能將某一特定領域的發展直覺地呈現出來，如此若能將這些分析方式配合視覺化的呈現，則研究學者便能更進一步了進行大量文獻資料視覺化的分析、探索。 本研究試提出一個新的協助文獻探索平台系統架構，將傳統的文字搜尋轉變為視覺化的資料探索。使用者能透過三種不同的層級的資料：知識本體與關鍵字層、引文網路層及人員網路層，並與呈現的資料互動進一步了解資料間的關聯方式。最後實作視覺化雛型平台，並使用在國家圖書館所提供的博、碩士論文網所提供的論文資料，提供給研究人員探索特定知識領域中新研究方向的探索工具，並能協助研究者能在尚未完瞭解的專業領域之前，能快速地瞭解在該其領域重要文獻的導引平台。 / Paper survey is the most important task for building earnest theories, while researchers conducting academic researches. One must touches the fundamental detail of each theory and track down the develop-path of what achievement have been established by previous researches. Benefit from synergy of information age and document digitalized, it not only reduces the cost of finding reference documents, but also makes researchers suffer from information overwhelming after click single “search it” bottom. Stand in for traditional paper web search methods, new academic paper search technology borrowing from the idea of web search engine calculates the importance of each paper by cited number, and recommends users the most important papers by serial listing. However, serial listing does never spell the relationships of suggesting papers out, but only those results match some specific criteria. Those relationships of papers can be classified into 3 different types: the relations of keywords and references that author used and social relationship of authors like co-author and author co-citation which have been developed to explain the complex citation network structures. Those multi-dimensional relationships are extremely abundant and complex, so there is no better way to deal with but depending on visual data processing within human nature. In this paper, we try to propose a new platform to transform paper search in serial listing, into a visualized explore platform by demonstrating 3 different types of relationship: ontology-keywords, papers-references and personnel-references. End users can fallow the relationships between each difference nodes to explore considerable references, as well as change into different view and interact with existing information by using interactive mechanizes. In order to bring this idea to practical application usage, we build a proto-type platform to show our idea by using data from ETDS (electronic theses and dissertations system) of Ministry of education. We hope sincerely by using this proto-type platform, users can catch the major ideas of specific knowledge domain and researchers can explore acceptable references and even conduct new search topic.

Submitted by Joaquim Heck (joaquimheck@uol.com.br) on 2018-03-06T23:24:07Z No. of bitstreams: 1 PDF_CDAE_Tese_Joaquim_Heck.pdf: 10473820 bytes, checksum: ad23dda74b39a1cdacaf8ac5345e3c9d (MD5) / Approved for entry into archive by Debora Nunes Ferreira (debora.nunes@fgv.br) on 2018-03-07T12:33:18Z (GMT) No. of bitstreams: 1 PDF_CDAE_Tese_Joaquim_Heck.pdf: 10473820 bytes, checksum: ad23dda74b39a1cdacaf8ac5345e3c9d (MD5) / Made available in DSpace on 2018-03-07T12:39:47Z (GMT). No. of bitstreams: 1 PDF_CDAE_Tese_Joaquim_Heck.pdf: 10473820 bytes, checksum: ad23dda74b39a1cdacaf8ac5345e3c9d (MD5) Previous issue date: 2018-02-23 / Esta tese apresenta os resultados de um estudo em metodologia que estabeleceu um novo método específico de análise documental e histórica, denominado cartografia estrutural para revisão de literatura, e que foi aplicado de forma experimental no estudo histórico de um tema da área de Pesquisa Operacional, os Problem Structuring Methods (PSMs). A cartografia estrutural para revisão de literatura é um método rigoroso e interdisciplinar que revela a estrutura subjacente à uma literatura por meio dos conceitos metodológicos encontrados em pesquisa documental, pesquisa histórica, Teoria dos Grafos, Análise de redes de citações e Cartografia. Verificou-se, a partir dessa interdisciplinaridade, que as relações entre diferentes autores, de diferentes épocas e que publicaram em diferentes periódicos são essenciais para aprofundar a compreensão e familiarizar o pesquisador com uma determinada área do conhecimento. Depois de introduzir a natureza básica da indexação de citações e da análise de rede, esse estudo apresenta a elaboração de um atlas bibliográfico, formado por mapas visuais e diagramas. A aplicação desse método resultou na composição do atlas bibliográfico sobre os PSMs no período de 1950 a 1989. O mapeamento de ideias e conceitos em um arranjo organizado, obtido pela análise exploratória de uma rede de citações longitudinal, assegura familiaridade com os conceitos constitutivos dos PSMs. O atlas bibliográfico é constituído por três análises principais: a exploração da abrangência panorâmica, a das referências e a dos periódicos. Esse estudo fornece subsídios para uma reflexão importante sobre a noção de unidade dos PSMs e a coesão de seus conceitos constituintes encontrada em seus precedentes teóricos. Finalmente, o método de cartografia estrutural possibilita realizar uma revisão de literatura sofisticada, bem como identificar temas fundamentais para futuras pesquisas mais produtivas. / This thesis presents the results of a methodological study that has established a specific method of historical and documental investigation, denominated structural cartography for literature review, which was experimentally applied in the study of Problem Structuring Methods (PSMs) history. Structural cartography for literature review is a rigorous and interdisciplinary method that reveals the underlying structure of a field literature through the methodological notions found in historical research, documental investigation, Graph Theory, and its subfield of citation network analysis, and Cartography. Due to its interdisciplinarity, it becomes clear that the relations between different authors, from different periods of time and from different media are essential to familiarize scholars with their fields of knowledge. After introducing the basic concepts of citation indexing and network analysis, this study shows how to build a bibliographic atlas composed of visual maps and diagrams. The application of this method resulted in the bibliographic atlas on the PSMs from 1950 to 1989. The mapping of ideas and concepts in an organized arrangement, which is obtained by the analysis of a longitudinal exploratory citation network, ensures familiarity with primitive concepts. The bibliographic atlas is composed by three main analysis: the exploration of coverage, of sources and of media. Moreover, this study provided insights for an important reflection on the notion of PSMs unity and the cohesion of their constituent concepts found in their theoretical precedents. Finally, the method of structural cartography for literature review made possible to conduct sophisticate literature review and has provided substantial themes for future research.