Literature explorer: effective retrieval of scientific documents through nonparametric thematic topic detection

Shaopeng Wu, Youbing Zhao, Farzad Parvinzamir, Nikolaos Th. Ersotelos, Hui Wei, Feng Dong

Research output: Contribution to journalArticle

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Abstract

Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover “thematic topics” from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.
Original languageEnglish
Number of pages18
JournalThe Visual Computer
Early online date2 Aug 2019
DOIs
Publication statusE-pub ahead of print - 2 Aug 2019

Keywords

  • topic explorer
  • data visualisation
  • topic modelling
  • text mining
  • web application
  • scientific documents

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