Whyis is a nano-scale knowledge graph publishing, management, and analysis framework.
Whyis is available as both a python package and a docker image. When deploying to a server, the generated knowlege graph application (kgapp) can easily be installed in recent Ubuntu versions. Since the kgapp is source-based, it can be managed using source control systems like Git or others.
Installing and running Whyis 2.0 is very straightforward. It is available for installation on either Ubuntu 18 or later, MacOS, or the Windows Subsystem for Linux.
Requirements:
We will refer to your most recent python as python, although most systems only use the python alias for python 2.x. Please use the command for your version of python.
NOTE: It is strongly recommended to install Whyis into a virtual python environment using venv, virtualenv, Conda, or similar. The following will generate and activate a venv for your current python. Please refer to your virtual environment documentation to generate it if you are not using venv.
python -m venv venv
source venv/bin/activate
Finally, install the whyis package using pip:
pip install whyis
To generate your first kgapp, make a directory for it, change to that directory, and start whyis:
mkdir kgapp
cd kgapp
whyis
This will generate all the needed files to build a knowledge graph, and start the SPARQL database, inference engine system, and the web server. Visit the web page at http://localhost:5000 to start working with Whyis.
Running whyis in docker is also very simple. With docker installed, To generate your first knowledge graph, make a directory for your kgapp, change to it, and start the whyis container:
mkdir kgapp
cd kgapp
docker pull tetherlessworld/whyis
docker run -v $PWD:$PWD -w $PWD -p 5000:5000 --name mykgapp tetherlessworld/whyis:latest
Note that $PWD binds the current directory as the working dir in the container. You can adjust this as needed. Once the container is running, you can visit the knowledge graph at http://localhost:5000.
Whyis commands can be run from inside the container using docker exec.
If you need to deploy your knowledge graph to a server, copy your kgapp dir to the server, and run the following scripts from within the kgapp dir:
./script/bootstrap
./script/setup