We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature, that was semi-automatically constructed using NLP methods. GrapAL satisfies a variety of use cases and information needs requested by researchers. At the core of GrapAL is a Neo4j graph database with an intuitive schema and a simple query language. In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities and computing citation-based metrics. We open source the demo code to help other researchers develop applications that build on GrapAL.
@article{arxiv.1902.05170,
title = {GrapAL: Connecting the Dots in Scientific Literature},
author = {Christine Betts and Joanna Power and Waleed Ammar},
journal= {arXiv preprint arXiv:1902.05170},
year = {2019}
}