Visual Exploration of Literature Using Connected Papers: A Practical Approach

Abstract

The paper aims to examine the visual exploration tool “Connected Papers” (www.connectedpapers.com), which identifies relevant literature based on content similarities and displays the result in the form of visual clusters. Connected Papers searches through Semantic Scholar literature corpus and discovers the most relevant related research papers using a specialized algorithm. The researcher will have to identify the most relevant paper as an “origin paper” among the retrieved papers. The origin paper acts as a base for the formation of a literature graph based on similarities. The unique features (i.e. “Prior” and “Derivate”) help a researcher to identify the most relevant literature. In this paper, the topic “Use of drones in Agriculture” is selected to demonstrate the process of literature exploration using Connected Papers. A set of relevant papers is retrieved, out of which the researcher will have to select one of the most appropriate relevant papers. That paper is termed as the origin paper to create the visualization of literature. Connected Papers suggests the most relevant papers based on the search keywords, but the graph is solely based on a researcher's judgement when selecting an origin paper. As explained in the paper, the researcher of other domains may adopt the process to understand the literature mapping phenomena for their own discipline.