README.md

Covid-19 Semantic Search Engine Website

This Project is part of Society Impact Project - Electronics and Electrical Communication Department - Faculty of Engineering - Cairo University - Dr. Hassan Mostafa

Developed a website like a search engine for the medical community to help them in their fight against COVID-19 Under Supervision of Dr. Hassan Mostafa

This Repo contains a website for a COVID-19 Search Engine that can be used by Medical community to search for topics in covid-19 published papers, it's based on LDA and trained on +40K papers

Dataset - COVID-19 Open Research Dataset (CORD-19):

COVID-19 Open Research Dataset (CORD-19) is a free resource of scholarly articles, aggregated by a coalition of leading research groups, about COVID-19 and the coronavirus family of viruses. The dataset can be found on Semantic Scholar and there is a research challenge on Kaggle.

This project builds an index over the CORD-19 dataset to assist with analysis and data discovery. A series of tasks were explored to identify relevant articles and help find answers to key scientific questions on a number of COVID-19 research topics.

Tasks

The following files show the top query results for each task provided in the CORD-19 Research Challenge using this model. A highlights section is also shown for each task, which highlights the most relevant sentences from the query results.

• What is known about transmission, incubation, and environmental stability?
• What do we know about COVID-19 risk factors?
• What do we know about virus genetics, origin, and evolution?
• What do we know about vaccines and therapeutics?
• What do we know about non-pharmaceutical interventions?
• What has been published about medical care?
• What do we know about diagnostics and surveillance?
• What has been published about information sharing and inter-sectoral collaboration?
• What has been published about ethical and social science considerations?

Installation

You can use Git to clone the repository from GitHub and install it.

Python 3.5+ is supported

Building a model

Download all the files in the Download CORD-19 section on Semantic Scholar. Go the directory with the files and run the following commands.

cd <download_path>

For each tar.gz file run the following mkdir && tar -C -xvzf <file.tar.gz>

Once completed, there should be a file name metadata.csv and subdirectories for each data subset with all json articles.

To build the model locally:

# run loader.py to prepare the dataset
python -m loader.py 

# Build model files 
python -m model.py

The model will be stored in the same directory

Tech Overview

The model is a built on LDA and using CountVectorizer

Please find attached the video with a demo for the website working at this link

Under Supervision of:

• Dr Hassan Mostafa

Submitted by:

• Abdallah Ahmed
• Abdelrahman Ahmed
• Mohamed Sabry
• Mohamed Abd Elhalim
• Youssef Mostafa