Installing the reconciliation service

Requirements

The Wikibase instance should have:

In addition it is also recommended that the Wikibase instance uses the CirrusSearch extension (ElasticSearch-based search engine).

Configuration

The configuration of the service is done in a Python file config.py. A sample configuration file is provided for Wikidata, config_wikidata.py.

Installing with Docker

You can run this service with docker. First, clone the repository and go to its root directory:

git clone https://github.com/wetneb/openrefine-wikibase
cd openrefine-wikibase

Then, copy the sample config_docker.py to config.py and modify the copy to point to the Wikibase instance of your choice.

Finally, start the service:

docker-compose up

On Windows you will need to accept the Windows Firewall popup to expose the port 8000 where the service runs.

You can then access the landing page of your new reconciliation service at http://localhost:8000/.

To use it in OpenRefine, you can add the reconciliation service (in the “Start reconciling” dialog) with the address “http://localhost:8000/en/api”. You can then use this reconciliation service to match data to items stored in your Wikibase instance.

Installing manually

It is possible to run this web service locally. You will need Python 3.7 or later and a redis instance.

  • Clone this repository, either with git (git clone https://github.com/wetneb/openrefine-wikibase) or by downloading the repository from Github as an archive

  • It is recommended to set up a virtualenv to isolate the dependencies of the software from the other python packages installed on your computer. On a UNIX system, python3 -m venv .venv and source .venv/bin/activate will do. On a Windows system, python.exe -m venv venvname followed by venvnameScriptsactivate should work.

  • Install Python3 development packages (libpython3-dev on Debian based systems)

  • Install the Python dependencies with pip install -r requirements.txt

  • Copy the configuration file provided: cp config_wikidata.py config.py (copy config_wikidata.py config.py on Windows)

  • Edit the configuration file config.py so that redis_client contains the correct settings to access your redis instance. The default parameters should be fine if you are running redis locally on the default port.

  • Finally, run the instance with python app.py (for development purposes). The service will be available at http://localhost:8000/en/api.

On Debian-based systems, it looks as follows:

sudo apt install git redis-server python3 virtualenv libpython3-dev
git clone https://github.com/wetneb/openrefine-wikibase
cd openrefine-wikibase
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Deploying in production

To run this service in production, we recommend using gunicorn in conjunction with uvicorn. Those packages can be installed in the same virtual environment as the code, with pip install gunicorn uvicorn.

The web service can then be run with gunicorn app:app -b localhost:8080 –workers 4 –worker-class uvicorn.workers.UvicornWorker.

Since this process needs to keep running, you should deploy it appropriately, for instance in a Kubernetes pod or as a systemd service. Here is an example systemd service configuration file, stored in /etc/systemd/system/wdrecon.conf:

[Unit]
Description=Wikidata reconciliation service
After=network.target

[Service]
Type=simple
User=wdrecon
Group=wdrecon
Restart=always
EnvironmentFile=-/etc/default/wdrecon
WorkingDirectory=/home/wdrecon/openrefine-wikibase/
ExecStart=/bin/sh -c '${WDRECON_GUNICORN_BIN} app:app -b localhost:8080 --workers ${WDRECON_WORKERS} --worker-class uvicorn.workers.UvicornWorker'

[Install]
WantedBy=multi-user.target

This is accompanied by the following environment file, stored at /etc/default/wdrecon:

WDRECON_GUNICORN_BIN="/home/wdrecon/venv/bin/gunicorn"
WDRECON_WORKERS="4"

For the Wikidata service, we run multiple instances of such a gunicorn server, gathered together behind an Apache load balancer.