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.

In several real-life data-mining... This paper proposes a relationship-based approach that alleviates both problems, side-stepping the "curse-of-dimensionality" issue by working in a suitable similarity space instead of the original high-dimensional attribute space. This intermediary similarity space can be suitably tailored to satisfy business criteria such as requiring customer clusters to represent comparable amounts of revenue. We apply efficient and scalable graph-partitioning-based clustering techniques in this space. The output from the clustering algorithm is used to re-order the data points so that the resulting permuted similarity matrix can be readily visualized in two dimensions, with clusters showing up as bands. While two-dimensional visualization of a similarity matrix is by itself not novel, its combination with the order-sensitive partitioning of a graph that captures the relevant similarity measure between objects provides three powerful properties: (i) the high-dimensionality of the data does not affect further processing once the similarity space is formed; (ii) it leads to clusters of (approximately) equal importance, and (iii) related clusters show up adjacent to one another, further facilitating the visualization of results. The visualization is very helpful for assessing and improving clustering. For example, actionable recommendations for splitting or merging of clusters can be easily derived, and it also guides the user toward the right number of clusters

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 article presents two studies concerning the role of individual differences in searching through a spatialsemantic virtual environment. In the first study, 10 subjects searched for two topics through a spatial user interface of a semantic space. A strong positive correlation was found between associative memory (MA-1) and search performance (r # 0.855, p # 0.003), but no significant correlation was found between visual memory (MV-1) and search performance. In the second study, 12 subjects participated in a within-subject experimental design. The same spatial user interface and a simple textual user interface were used. The effects of spatial ability (VZ-2), associative memory (MA-1), and on-line experience were tested on a set of interrelated search performance scores. A statistically significant main effect of on-line experience was found, F(6, 4) # 6.213, p # 0.049, two-tailed. In particular, on-line experience has a significant effect on the recall scores with the textual interface. Individuals experienced in on-line search are more likely to have a higher recall score with the textual interface than less experienced individuals. No significant main effects were found for spatial ability and associative memory. Subjects' comments suggest a potentially complex interplay between individuals' mental models and the high-dimensional semantic model. Qualitative and process-oriented studies are, therefore, called for to reveal the complex interaction between individuals' cognitive abilities, domain knowledge, and direct manipulation skills. A recommendation is made that spatial-semantic models should be adaptable to suit individuals and tasks at various levels

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

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.

Introduction When we first encounter a scientific discipline, or a subject domain, we often would need to have a good standing point and as many signposts as possible to guide ourselves through the field. On the other hand, more experienced researchers and domain experts would need effective ways to track the development of their own fields and extract crucial signs of the dynamics of a scientific discipline (Bush, 1945). The World Wide Web (Web) has revolutionized the way we search for information. On today's Web we can easily access a vast amount of information on almost any subject. However, a profound challenge to many of us in the modern information society is to transcend the vast amount of information in scientific literature and access scientific knowledge at a higher level. The meta-knowledge, the knowledge of how particular knowledge structures have been perceived, should become an integral part of the scientific discipline involved, and it should be presented with simplicity and clarity for scholarly communication as well as public understanding. Domain visualization is an exciting field of study that addresses these q

The past year has seen a dramatic increase in the amount of social information published in RDF documents. Our investigations [1, 2] show that the Friend of a Friend (FOAF) ontology [3] is among the most used semantic web ontologies. This is true if we measure the number of semantic web documents (SWDs) that use the FOAF namespace, as Table I