Interactive Exploration and Discovery of Scientific Publications with PubVis

TL;DR

PubVis is an interactive web application that leverages machine learning to enhance exploration and discovery of scientific publications through visualization, personalized recommendations, and full-text search, aiding researchers in navigating large literature collections.

Contribution

The paper introduces PubVis, a novel open-source web tool that combines visualization, machine learning-based recommendations, and full-text search for improved scientific literature exploration.

Findings

Provides an interactive visualization of 10,000 papers

Enables personalized article recommendations

Offers an easy-to-deploy local setup

Abstract

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.