Interactive Exploration and Discovery of Scientific Publications with PubVis
This addresses the challenge for researchers overwhelmed by the growing volume of scientific papers, offering an incremental improvement over existing keyword-based search tools.
The authors tackled the problem of exploring and discovering scientific publications by developing PubVis, a web application that uses machine learning to provide interactive visualizations, personalized recommendations, and advanced search, resulting in a tool that can handle up to 10,000 papers in a demo version.
With an exponentially growing number of scientific papers published each year, advanced tools for exploring and discovering publications of interest are becoming indispensable. To empower users beyond a simple keyword search provided e.g. by Google Scholar, we present the novel web application PubVis. Powered by a variety of machine learning techniques, it combines essential features to help researchers find the content most relevant to them. An interactive visualization of a large collection of scientific publications provides an overview of the field and encourages the user to explore articles beyond a narrow research focus. This is augmented by personalized content based article recommendations as well as an advanced full text search to discover relevant references. The open sourced implementation of the app can be easily set up and run locally on a desktop computer to provide access to content tailored to the specific needs of individual users. Additionally, a PubVis demo with access to a collection of 10,000 papers can be tested online.