KG-Enricher

KG-Enricher is a web service built to enrich knowledge graphs generated by the News Hunter platform, part of the News Angler project.

Build and run Docker image

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Build the Docker image

Navigate to the root of the repository and run:

docker build -t <image name> .

Run the Docker image

docker run -p 80:80 <environment variables> <image name>

The image expects the following environment variables:

Variable	Explanation	Example Value
yago_endpoint	Yago4 SPARQL endpoint	https://yago-knowledge.org/sparql/query
yago_endpoint_max_connections	Maximum number of concurrent requests to the Yago4 endpoint	10
wd_endpoint	Wikidata SPARQL endpoint	https://query.wikidata.org/sparql
wd_endpoint_max_connections	Maximum number of concurrent requests to the Wikidata endpoint	5

The environment variables can be supplied as arguments or with a file.

Docker run example with aruments

docker run -p 80:80 --env yago_endpoint=https://yago-knowledge.org/sparql/query --env yago_endpoint_max_connections=10 --env wd_endpoint=https://query.wikidata.org/sparql --env wd_endpoint_max_connections=5 <image name>

Docker run example with env file

docker run -p 80:80 --env-file .\example.env <image name>

Run application outside Docker

Navigate to the root of the repository before proceeding.

(Optional) Create a virtual environment for the application

Create the environment:

py -m venv env

Then activate it:

.\env\Scripts\activate

Install dependencies

pip install -r dev-requirements.txt

Run from command line

You can set the environment variables in your shell or use a file like in the Docker example.

First navigate to the app directory:

cd app

Then run uvicorn to start the app:

uvicorn main:app --reload --port 8000

You can also provide an env file for the environment variables:

uvicorn main:app --reload --port 8000 --env-file <path to env file>

Debug in VS Code

It is also possible to debug the app in VS Code. This makes it possible to set breakpoints, etc. But keep in mind that it has a major performance impact.

All you need to do is to create a launch configuration for the project and add the following configuration to it:

{
    "name": "Python: FastAPI",
    "type": "python",
    "request": "launch",
    "module": "uvicorn",
    "args": [
        "main:app",
        "--reload",
        "--port",
        "8000"
    ],
    "env": {
        "yago_endpoint": "https://yago-knowledge.org/sparql/query",
        "yago_endpoint_max_connections": "5",
        "wd_endpoint": "https://query.wikidata.org/sparql",
        "wd_endpoint_max_connections": "5",
    },
    "console": "integratedTerminal",
    "cwd": "${workspaceFolder}/app"
}

You can of course customize the properties in "env" to your liking.