Abstract:

Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an enormous scientific effort from different domains. Resulting publications formed a gigantic domain-specific collection of text in which finding studies on a biomolecule of interest is quite challenging for general purpose search engines due to terminology-rich characteristics of the publications. Here, we present Vapur, an online COVID-19 search engine specifically designed for finding related protein - chemical pairs. Vapur is empowered with a biochemically related entities-oriented inverted index in order to group studies relevant to a biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/.

Citation

Please cite the following paper if you use Vapur on your research: Arxiv
Download BibTex
@inproceedings{koksal2020vapur,
title={Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature},
author={Köksal, Abdullatif and Dönmez, Hilal and Özçelik, Rıza and Ozkirimli, Elif and Özgür, Arzucan},
booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020},
year={2020}
}