Discovery and visualization of expertise in a scientific community (original) (raw)

An Interactive Analytic Tool for Peer-Review Exploration

This paper presents an interactive analytic tool for educational peer-review analysis. It employs data visualization at multiple levels of granularity, and provides automated analytic support using clustering and natural language processing. This tool helps instructors discover interesting patterns in writing performance that are reflected through peer reviews.

Visualizing a Discipline: An Author Co-Citation Analysis of Information Science, 1972-1995

Journal of the American Society for …, 1998

in articles, regardless of which of their works are cited. discipline-information science-in terms of its au-ACA synthesizes many such counts. Now that 15 years thors. Names of those most frequently cited in 12 key have passed since it was introduced by White and Griffith journals from 1972 through 1995 were retrieved from So-(1981), the present writers wish to explore this literaturecial Scisearch via DIALOG. The top 120 were submitted based technique as a means for contributing to intellectual to author co-citation analyses, yielding automatic classifications relevant to histories of the field. Tables and history. As in that earlier article, we shall use authors graphics reveal: (1) The disciplinary and institutional affrom information science to illustrate that, although ACA filiations of contributors to information science; (2) the is applicable in any discipline (Bayer, Smart, & Mcspecialty structure of the discipline over 24 years; (3)

Interactive Overlays of Journals and the Measurement of Interdisciplinarity

2013

Using "Analyze Results" at the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Science Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy." Since this indicator of interdisciplinarity is normalized between zero and one, the interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel et al.'s (2008) community-finding algorithms operating on the relations journals included in JCRs. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started and/or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal literatures are further discussed: local and global maps are based on different assumptions and can be expected to serve different purposes for the explanation.

Using word clouds for fast identification of papers' subject domain and reviewers' competences

ArXiv, 2021

Generating word (tag) clouds is a powerful data visualization technique that allows people to get easily acquainted with the content of a large collection of textual documents and identify their subject domains for a matter of seconds, without reading them at all. This paper suggests applying word clouds visualization to conference management systems (specialized document management and decision support systems) in order to support and facilitate decision making in at least two important processes – forming the Programme Committee by inviting suitable reviewers and manual (re)assignment of reviewers to papers. Word clouds proved to be very useful tool for fast identification of papers’ subject domain and reviewers’ competences.

Expertise Recommender System for Scientific Community

2011

Finding experts in academics as well as in enterprises is an important practical problem. Both manual and automated approaches are employed and have their own pros and cons. On one hand, the manual approaches need extensive human efforts but the quality of data is good, on the other hand, the automated approaches normally do not need human efforts but the quality of service is not as good as in the manual approaches. Furthermore, the automated approaches normally use only one metric to measure the expertise of an individual. For example, for finding experts in academia, the number of publications of an individual is used to discover and rank experts. This paper illustrates both manual and automated approaches for finding experts and subsequently proposes and implements an automated approach for measuring expertise profile in academia. The proposed approach incorporates multiple metrics for measuring an overall expertise level. To visualize a rank list of experts, an extended hyperbolic visualization technique is proposed and implemented. Furthermore, the discovered experts are pushed to users based on their local context. The research has been implemented for Journal of Universal Computer Science (J. UCS) and is available online for the users of J. UCS.

SEMEF: A Taxonomy-Based Discovery of Experts, Expertise and Collaboration Networks

Finding relevant experts in research is often critical and essential for col- laboration. Semantics can be useful in this process. In particular, a taxonomy of Computer Science topics, together with an ontology of publications can be glued through explicit relationships from papers to one or more topics. These paper-to- topics relationships, extracted from paper abstracts and keywords are valuable for building an Expertise Profile for a researcher based on the aggregation of the topics of his/her publications. We describe an approach that finds experts, expertise and collaboration networks in the context of the peer-review process. We use DBLP bib- liography data to determine different levels of collaboration based on degrees of separation. This helps in suggesting experts for PC membership and has the benefit of presenting potentially unknown experts to the PC Chair(s). We present our find- ings and evaluations in the context of expanding collaboration networks in a peer- review se...

Visualization of Disciplinary Profiles: Enhanced Science Overlay Maps

Journal of Data and Information Science

The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps.We use the combined set of 2015Findings appear in the form of visuals throughout the manuscript. In Figures 1–9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies.As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science.Visualization of science overlay maps is now more accurate and true to the 2015The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent

LRV: A Tool for Academic Text Visualization to Support the Literature Review Process

Computers, Materials & Continua, 2019

Text visualization is concerned with the representation of text in a graphical form to facilitate comprehension of large textual data. Its aim is to improve the ability to understand and utilize the wealth of text-based information available. An essential task in any scientific research is the study and review of previous works in the specified domain, a process that is referred to as the literature survey process. This process involves the identification of prior work and evaluating its relevance to the research question. With the enormous number of published studies available online in digital form, this becomes a cumbersome task for the researcher. This paper presents the design and implementation of a tool that aims to facilitate this process by identifying relevant work and suggesting clusters of articles by conceptual modeling, thus providing different options that enable the researcher to visualize a large number of articles in a graphical easy-to-analyze form. The tool helps the researcher in analyzing and synthesizing the literature and building a conceptual understanding of the designated research area. The evaluation of the tool shows that researchers have found it useful and that it supported the process of relevant work analysis given a specific research question, and 70% of the evaluators of the tool found it very useful.

PaperVis: Literature Review Made Easy

Computer Graphics Forum, 2011

Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.

Collecting expertise of researchers for finding relevant experts in a peer-review setting

1st International ExpertFinder …, 2007

We present ideas for determining the expertise of researchers across various areas of computer science and for finding relevant experts/reviewers in a peerreview setting. We explain how Semantic Web techniques for data collection and data representation using ontologies can be used in addressing this specific "ExpertFinder" problem.