# GrapAL: Connecting the Dots in Scientific Literature

## TL;DR

GrapAL is a semi-automatically constructed graph database of scientific literature that enables researchers to explore, analyze, and discover connections within scientific knowledge using NLP and graph queries.

## Contribution

This paper introduces GrapAL, a versatile, open-source graph database for scientific literature built with NLP techniques, supporting various research use cases.

## Key findings

- Supports finding experts on specific topics
- Enables discovering indirect biomedical connections
- Calculates citation-based metrics

## Abstract

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.