Abstract not found

This article describes the latest development of a generic approach to detecting and visualizing emerging trends and transient patterns in scientific literature. The work makes substantial theoretical and methodological contributions to progressive knowledge domain visualization. A specialty is conceptualized and visualized as a time-variant duality between two fundamental concepts in information science – research fronts and intellectual bases. A research front is defined as an emergent and transient grouping of concepts and underlying research issues. The intellectual base of a research front is its citation and co-citation footprint in scientific literature – an evolving network of scientific publications cited by research front concepts. Kleinberg’s burst detection algorithm is adapted to identify emergent research front concepts. Freeman’s betweenness centrality metric is used to highlight potential pivotal points of paradigm shift over time. Two complementary visualization views are designed and implemented: cluster views and time-zone views. The contributions of the approach are: 1) the nature of an intellectual base is algorithmically and temporally identified by emergent research-front terms, 2) the value of a co-citation cluster is explicitly interpreted in terms of research front concepts and 3) visually prominent and algorithmically detected pivotal points substantially reduce the complexity of a visualized network. The modeling and visualization process is implemented in CiteSpace II, a Java application, and applied to the analysis of two research fields: mass extinction (1981-2004) and terrorism (1990-2003). Prominent trends and pivotal points in visualized networks were verified in collaboration with domain experts, who are the authors of pivotal-point articles. Practical implications of the work are discussed. A number of challenges and opportunities for future studies are identified.

To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape.

AB AB AB Org IN AF AD Source JN SO SO Year parse from PB PY DP Type DT PT PT Title TI TI TI Author AU AU AU Terms DE DE MH Table 3. Number of articles kept from each data source in combined data set.

This paper presents a new map representing the structure of all of science, based on journal articles, including both the natural and social sciences. Similar to cartographic maps of our world, the map of science provides a bird’s eye view of today’s scientific landscape. It can be used to visually identify major areas of science, their size, similarity, and interconnectedness. In order to be useful, the map needs to be accurate on a local and on a global scale. While our recent work has focused on the former aspect, 1 this paper summarizes results on how to achieve structural accuracy. Eight alternative measures of journal similarity were applied to a data set of 7,121 journals covering over 1 million documents in the combined Science Citation and Social Science Citation Indexes. For each journal similarity measure we generated two-dimensional spatial layouts using the force-directed graph layout tool, VxOrd. Next, mutual information values were calculated for each graph at different clustering levels to give a measure of structural accuracy for each map. The best co-citation and inter-citation maps according to local and structural accuracy were selected and are presented and characterized. These two maps are compared to establish robustness. The inter-citation map is then used to examine linkages between disciplines. Biochemistry appears as the most interdisciplinary discipline in science.

this article, we focus on the use of a particular approach to visualizing and tracking the growth of scientific paradigms. We illustrate the potential of this approach with two case studies. The first case study investigates the role of information visualization in tracking the growth of the study of mass extinctions. The second case study tracks down the line of research concerning whether there is a connection between mad cow disease and new variant Creutzfeldt-Jakob disease (vCJD). The rest of the article is organized as follows: we first provide a brief introduction to the key concepts and principles. Then we explain how our approach works and what types of structural and visual properties we should look for in the case studies. We describe two case studies in detail. We finally reflect on our experience with these case studies in the broader context of knowledge tracking and technology monitoring

journals) are made accessible from the perspective of any of these journals. A vector-space model is used for normalization, and the results are brought online at

The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this article, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining. 1

The paper introduces an approach that allows one to visualize the semantic structure of retrieval results for browsing. Latent Semantic Analysis as well as cluster techniques are applied to extract salient semantic structures and citation patterns automatically. A modified Boltzman algorithm is used to spatially visualize co-citation patterns and semantic similarity networks of retrieved documents for interactive exploration. The approach was implemented to visualize retrieval results from two different databases: the Science Citation Index Expanded and the Dido Image Bank.

Measuring the relatedness between bibliometric units (journals, documents, authors, or words) is a central task in bibliometric analysis. Relatedness measures are used for many different tasks, among them the generating of maps, or visual pictures, showing the relationship between all items from these data. Despite the importance of these tasks, there has been little written on how to quantitatively evaluate the accuracy of relatedness measures or the resulting maps. The authors propose a new framework for assessing the performance of relatedness measures and visualization algorithms that contains four factors: accuracy, coverage, scalability, and robustness. This method was applied to 10 measures of journal–journal relatedness to determine the best measure. The 10 relatedness measures were then used as inputs to a visualization algorithm to create an additional 10 measures of journal–journal relatedness based on the distances between pairs of journals in two-dimensional space. This second step determines robustness (i.e., which measure remains best after dimension reduction). Results show that, for low coverage (under 50%), the Pearson correlation is the most accurate raw relatedness measure. However, the best overall measure, both at high coverage, and after dimension reduction, is the cosine index or a modified cosine index. Results also showed that the visualization algorithm increased local accuracy for most measures. Possible reasons for this counterintuitive finding are discussed.