Mapping Research Trajectories

• URL: http://arxiv.org/abs/2204.11859v1
• Date: Mon, 25 Apr 2022 13:32:39 GMT
• Title: Mapping Research Trajectories
• Authors: Bastian Sch\"afermeier, Gerd Stumme, Tom Hanika
• Abstract summary: We propose a principled approach for emphmapping research trajectories, which is applicable to all kinds of scientific entities. Our visualizations depict the research topics of entities over time in a straightforward interpr. manner. In a practical demonstrator application, we exemplify the proposed approach on a publication corpus from machine learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Steadily growing amounts of information, such as annually published scientific papers, have become so large that they elude an extensive manual analysis. Hence, to maintain an overview, automated methods for the mapping and visualization of knowledge domains are necessary and important, e.g., for scientific decision makers. Of particular interest in this field is the development of research topics of different entities (e.g., scientific authors and venues) over time. However, existing approaches for their analysis are only suitable for single entity types, such as venues, and they often do not capture the research topics or the time dimension in an easily interpretable manner. Hence, we propose a principled approach for \emph{mapping research trajectories}, which is applicable to all kinds of scientific entities that can be represented by sets of published papers. For this, we transfer ideas and principles from the geographic visualization domain, specifically trajectory maps and interactive geographic maps. Our visualizations depict the research topics of entities over time in a straightforward interpr. manner. They can be navigated by the user intuitively and restricted to specific elements of interest. The maps are derived from a corpus of research publications (i.e., titles and abstracts) through a combination of unsupervised machine learning methods. In a practical demonstrator application, we exemplify the proposed approach on a publication corpus from machine learning. We observe that our trajectory visualizations of 30 top machine learning venues and 1000 major authors in this field are well interpretable and are consistent with background knowledge drawn from the entities' publications. Next to producing interactive, interpr. visualizations supporting different kinds of analyses, our computed trajectories are suitable for trajectory mining applications in the future.

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