Defragmenting Research Areas with Knowledge Visualization and Visual Text Analytics
| Autor: | Roberto Therón, Alejandro Benito-Santos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
media_common.quotation_subject Sports science 02 engineering and technology lcsh:Technology Market fragmentation lcsh:Chemistry author-assigned keywords 0202 electrical engineering electronic engineering information engineering knowledge visualization General Materials Science Use case Instrumentation lcsh:QH301-705.5 media_common Fluid Flow and Transfer Processes Focus (computing) Creative visualization lcsh:T visual text analytics Process Chemistry and Technology General Engineering 020207 software engineering Statistical model distributional similarity Data science lcsh:QC1-999 Computer Science Applications Visualization lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Specialization (logic) methodology transfer 020201 artificial intelligence & image processing lcsh:Engineering (General). Civil engineering (General) problem-driven visualization research lcsh:Physics |
| Zdroj: | Applied Sciences Volume 10 Issue 20 Applied Sciences, Vol 10, Iss 7248, p 7248 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10207248 |
| Popis: | The increasing specialization of science is motivating the fragmentation of traditional and well-established research areas into interdisciplinary communities of practice that focus on cooperation between experts to solve problems in a wide range of domains. This is the case of problem-driven visualization research (PDVR), in which groups of scholars use visualization techniques in different application domains such as the digital humanities, bioinformatics, sports science, or computer security. In this paper, we employ the findings obtained during the development of a novel visual text analytics tool we built in previous studies, GlassViz, to automatically detect interesting knowledge associations and groups of common interests between these communities of practice. Our proposed method relies on the statistical modeling of author-assigned keywords to make its findings, which are demonstrated in two use cases. The results show that it is possible to propose interactive, semisupervised visual approaches that aim at defragmenting a body of research using text-based, automatic literature analysis methods. |
| Databáze: | OpenAIRE |
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