Rorissa, A. and Yuan, X. (2012). Visualizing and mapping the intellectual structure of information retrieval. Information Processing and Management, 48, 120-135.

Rorissa, A. and Yuan, X. (2012). Visualizing and mapping the intellectual structure of information retrieval. Information Processing and Management, 48, 120-135.

network analysis

Chen (2006)說明一個學術領域或學科的知識基礎(intellectual base)與研究前沿(research front)的區別：研究前沿是一個專業(specialty)當前最先進的技術狀態(state of the art)，由研究前沿引用所構成的部分則是它的知識基礎。通常在分析學術領域或學科的知識基礎時，大多利用期刊的引用資料，並且使用群集分析(cluster analysis)、多維分析(multidimensional analysis)與其他技術將引用資料的視覺化 (例如：Chen & Kuljis, 2003; Chen et al., 2010; Ding et al., 2000; McKechnie, Goodall, LajoiePaquette, & Julien, 2005; Tang, 2004; White & Griffith, 1981; White & McCain, 1998)。White (2003)則是使用尋徑網路(Pathfinder Networks)技術將White & McCain (1998)的資料繪製成圖書資訊學領域的科學映射圖。這些技術也都被應用於軟體工具的製作並且提供研究人員自由使用，例如CiteSpace。將引用資料等資訊繪製成科學映射圖的研究興趣提升可以歸納以下的原因：可使用的引用資料來源以及其他廣泛出現；許多提供視覺化與映射圖的電腦應用程式可以自由使用；對不斷增加數量的電子資料需要具有包容性與容易使用的管理與理解方法。

這篇研究利用2000到2009年間的資訊檢索領域的引用資料進行一系列的分析與視覺化，所利用的資料來源是Web of Science，共使用48,390筆書目紀錄，使用的視覺化工具是由Chen(2004a; 2004b; 2006)提出的CiteSpace (http://cluster.cis.drexel.edu/cchen/citespace/)。研究的項目與結果如下：

1) 作者合著網路(co-authorship network): 以32位發表10篇以上的較高生產力作者為分析對象，，其中一半來自於電腦科學，另一半則為資訊科學。只要兩位作者曾一起出現在一篇論文中便在他們的映射點間建立連結。網路建立好之後，測量每位作者映射點的中介性，找出中心作者。結果可以發現兩個作者數目超過八位的較大作者叢集，中心分別是 Jarvelin K和Chen HC，前者來自資訊科學，而後者則是來自電腦科學。本研究推論作者的生產力較高，同時也將有較多的合著對象。

2) 高被引的期刊與論文：在這個研究裡發現約有43%的高被引文獻是在1970到1989年間出版，顯示資訊檢索已是一個相當成熟的領域。另外，生產力較高的作者群與高被引文獻間的關連不明顯。引用最高的出版品與作者都是來自電腦科學，引用最高的前三名作者以及他們的引用次數佔全部引用數的比率分別是Salton (26.1%), Jansen (9.88%), and Baeza-Yates (8.38%)。

3)作者指定的關鍵詞(author-assigned keywords)：在關鍵詞所形成的共現網路裡，除了information retrieval，中介性較高的關鍵詞還包括information seeking、information system、evaluation和user studies。另外，在網路圖上可以明顯看出這些詞語形成的四個主要的研究集群為1)使用者研究(user studies)、網路資訊檢索(Web information retrieval)、3)引用分析/科學計量學(citation analysis/scientometrics)、和4)資訊檢索系統評估(information retrieval system evaluation)。

4) 活躍的機構(active institutions)：資訊檢索領域的主要研究機構大部分位於美國，並且絕大多數是學術機構或大學。利用作者的合著關係將這些研究機構的合作情形畫成網路圖，結果發現這個研究領域相當鼓勵跨機構和跨國的研究。

5) 來自於其他領域的想法(the import of ideas from other disciplines)：從論文的引用關係發現，資訊檢索研究的想法主要來自以下的五個領域：電腦科學(computer science)、圖書資訊學(library and information science)、工程(engineering)、電信傳播(telecommunications)和管理(management)，高達91.6%的引用來自這五個領域。

We analyzed citation data in the information retrieval subfield for the past decade (2000–2009) and presented the results in terms of co-authorship network, highly productive authors, highly cited journals and papers, author-assigned keywords, active institutions, and the import of ideas from other disciplines.

An indicator of the maturity of an area of inquiry is the growth in the number and quality of research publications (Van den Beselaar & Leydesdorff, 1996). Insight into the nature of a field can be gained by examining ‘‘the publications produced by its practitioners. To the extent that practitioners in the field publish the results of their investigations, this mode for assessing the state of a field can reflect with great specificity the content and problem orientations of the group. Of the many ways that publications can be analyzed and counted, perhaps the most revealing kind of data are the references cited by the practitioner group in their publications’’ (Small, 1981, p. 39).

A field, discipline, or other area of study can be broadly divided into an intellectual base and current research fronts (Chen, 2006). ‘‘If we define a research front as the state of the art of a specialty (i.e., a line of research), what is cited by the research front forms its intellectual base’’ (Chen, 2006, p. 360). Previous literature of a discipline cited in its current publications (i.e., its intellectual base) can inform us about current research fronts. It is through references to sources that authors make connections between concepts (Small, 1981). Collectively, such connections create a ‘‘representation of the cognitive structure of the research field’’ (Small, 1981, p. 39).

Although a number of studies have examined the literature of library and information science (e.g., Åström, 2007, 2010; Chen, Ibekwe-SanJuan, & Hou, 2010; Cronin & Meho, 2007; Donohue, 1972; Harter & Hooten, 1992; Harter, Nisonger, & Weng, 1993; Persson, 1994; Rice, 1990; White & McCain, 1998; Zhao & Strotmann, 2008a, 2008b), the nature of the literature concerning information retrieval has not been so thoroughly investigated (Ding, Chowdhury, & Foo, 2000; Ding, Yan, Frazho, & Caverlee, 2009; Ellis, Allen, & Wilson, 1999).

According to Chen (2006), the trends and patterns of scientific literatures provide researchers or communities of similar interests an overview of the related field(s) and relationships among the specific fields. More specifically, such information as the most influential articles or books, the evolvement of terms, noun phrases, keywords, the most reputed researchers, the connection between different institutions and countries over time can show trends and patterns that provide more overview.

The most prominent conclusions address the stable, multidisciplinary nature of the field. For instance, Persson (1994) found that, for a 5-year period (1986–1990), the intellectual base of the Journal of the American Society for Information Science (consisting of the most frequently co-cited authors) and its research front (consisting of articles sharing at least five cited authors) had similar maps (depicted in two-dimensional spaces). This finding highlights the stable nature of the topics explored by the information studies field.

Ding et al. (2000) conducted one of the earliest studies on the literature of information retrieval specifically. They analyzed co-citation data of 50 highly cited journals using multidimensional scaling and cluster analysis. They produced two-dimensional maps of the structure of the literature of information retrieval for an 11-year period (1987–1997). The visualizations revealed strong relationships between information retrieval and five other disciplines: computer science, physics, chemistry, psychology, and neuroscience. Their maps also show information retrieval as being part of both the computer science and LIS fields.

Tang (2004) identified the most common disciplines to which LIS exports ideas (based on the number of citations it received from the disciplines):
computer science, communication, education, management, business, and engineering. Another study looked at the export and import of ideas to and from LIS and found it to be an exporter of ideas (Cronin & Meho, 2008). That contrasts sharply with the state of the field 20 years ago when few researchers from other disciplines cited LIS literature (Cronin & Pearson, 1990).

Result

Analysis of authorship and co-authorship is critical to the understanding of scholarly communication and knowledge diffusion (Chen, 2006).

To show the extent of collaboration by the most productive researchers in our dataset, we used CiteSpace to create a coauthorship network map. Two researchers have co-authorship if they have co-authored at least one paper together. CiteSpace generates networks by measuring betweenness centrality scores. ‘‘In a network, the betweenness centrality of a node measures the extent to which the node plays a role in pulling the rest of the nodes in the network together. The higher the centrality of a node, the more strategically important the node is.’’ (Chen et al., 2009, p. 236).

Two of the author co-citation clusters are large enough to contain at least eight members/authors. Two highly productive authors (see Table 1) anchor the two clusters: Jarvelin K and Chen HC. ... These results point to a relation between collaboration frequency and the most productive authors. We are tempted to conclude that the more actively an author collaborates, the more productive she or he is. Further research is necessary to confirm this assertion.

Chen, Cribbin, Macredie, and Morar (2002) showed that visualization can be used to track the development of a scientific discipline and present the long-term process of its competing paradigms. They also assert that, among a discipline’s co-cited publications, the cluster consisting of the most highly cited publications may represent the discipline’s core or predominant paradigm.

A product of CiteSpace, Fig. 2 displays a document co-citation network, generated from the collective citing behavior in our information retrieval dataset. The network is composed of 121 reference nodes and 1163 co-citation links.

Author-assigned keywords can reveal specific focus areas of research in a field.

As suggested by Table 4, ‘‘information retrieval’’ is located near the center of the cloud of keywords. Fig. 4 also indicates that other related terms have taken on a central role in the subfield: ‘‘information seeking,’’ ‘‘information system,’’ ‘‘evaluation,’’ and ‘‘user studies.’’ This highlights the rising emphasis on user-centered system design and retrieval, as well as the importance of user studies in the evaluation of IR systems. Information retrieval research is stronger today because it has increasingly focused on user-centered design. Current user studies research is more about ‘‘users’ interaction with information retrieval systems than about user information behavior in general’’ (Zhao & Strotmann, 2008a, p. 2077).

Fig. 4 also suggests that the information retrieval subfield has its own special areas of inquiry. Four main clusters can be discerned on the visualization map, centered around user studies, Web information retrieval, citation analysis/scientometrics, and information retrieval system evaluation.

Fig. 5 maps the collaboration between the top 20 institutions of information retrieval authors in our dataset. ... These diverse groupings indicate that the information retrieval subfield encourages collaboration across institutions and countries.

Information retrieval researchers in our dataset cite primarily computer science and library and information science publications (see Table 6). Those two fields account for 82.79% of the citations. ... Apart from LIS and computer science, the third, fourth, and fifth other disciplines from which information retrieval imports ideas are engineering, telecommunications, and management, respectively. In fact, 91.6% of the citations by information retrieval authors whose articles were published between 2000 and 2009 were to these five disciplines.