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The aim of this project is to develop a web interface for visualizing indexed documents, enriched by exploiting the semantic index

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ISSA Visualization Web Application

The ISSA project focuses on the semantic indexing of scientific publications in an open archive.

This repository is a React-based web application meant to search articles from the ISSA knowledge base, and provide a visualization of their metadata. It uses the services offered by the backend application.

Installation

Pre-requisite: node.js 17, yarn.

Install the dependencies with yarn install.

Configuration

Configuration parameters are set in files .env.production and .env.development. In particular, REACT_APP_BACKEND_URL must be set to the URL of the backend application.

Run

Run the application in development mode: yarn start. By default the application listens on port 3001. This can be changed in file .env. Make sure the application is properly started by pointing your browser to:

http://localhost:3001/search

Note for Windows users: you may need to authorize the execution of scripts on Powershell (as admin): set-executionpolicy unrestricted

To deploy the application, run yarn build and copy the build directory to the web server where you want to host the application.

Logging

Log traces are printed out on the browser's console. It can be deactivated in file .env by setting property:

REACT_APP_LOG = off

License

See the LICENSE file.

Cite this work

Franck MICHEL, Youssef MEKOUAR, ISSA Project (2022). ISSA visualization web application. https://github.com/issa-project/web-visualization/.

Publications

ISSA: Generic Pipeline, Knowledge Model and Visualization tools to Help Scientists Search and Make Sense of a Scientific Archive. Anne Toulet, Franck Michel, Anna Bobasheva, Aline Menin, Sébastien Dupré, Marie-Claude Deboin, Marco Winckler, Andon Tchechmedjiev. 21st International Semantic Web Conference (ISWC), Oct 2022, Hangzhou, China. DOI: ⟨10.1007/978-3-031-19433-7_38⟩. HAL

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The aim of this project is to develop a web interface for visualizing indexed documents, enriched by exploiting the semantic index

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