Skip to content
A sample re-implementation of a guideline processing tool under a defined microservice architecture.
JavaScript Shell Prolog Dockerfile Other
Branch: prolog
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.


Type Name Latest commit message Commit time
Failed to load latest commit information.

Guideline Interaction

A sample re-implementation of a guideline processing tool under a defined microservice architecture.

Cite as A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools. Chapman, Martin and Curcin, Vasa. IEEE EUROCON 2019-18th International Conference on Smart Technologies. 2019.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.


Before installing, download and install Docker, Prolog, python, pip, virtualenv and Node.js.


Apache Jena Fuseki (Triple Store)

Run main container storage:

docker run --name fuseki-data -v /fuseki busybox

Run main container, supply port (3030, recommended), password and container store:

docker run -e ADMIN_PASSWORD=[Password] -d --name fuseki -p 3030:3030 --volumes-from fuseki-data stain/jena-fuseki

Navigate to http://localhost:3030, login with the username admin and the password as set above. select Manage Datasets, and create three new (persistent) datasets: drugs, transitions and beliefs.

TMR reasoner

Clone this repository:

git clone

Change into the backend directory:

cd backend

Clone the TMR repository:

git clone

Enter Prolog environment, and include address of Fuseki server as environment variable:

FUSEKI_HOST_PORT=http://localhost:3030/ swipl

Load server:

?- consult('').

Start server on a given port (1234, recommended):

?- server(1234).


Change to the API folder.

cd ../api

Create a node virtual environment (within a python virtual environment), and activate it:

virtualenv env
. env/bin/activate
pip install nodeenv
nodeenv nenv
. nenv/bin/activate

Install dependencies:

cat requirements.txt | xargs npm install -g

Create an environment file:

touch .env

Add the following information to this environment file using a text editor:


Where [Password] is the password you created for the triple store earlier.

Run server:

npm start

The server runs by default on port 8888.


See documentation.


Deployment is via Docker, and includes containers for this application (api and backend), Fuskei and an optional reverse proxy. If using the reverse proxy, fill in the appropriate configuration.

Build these containers:

docker-compose build

Run these containers:

docker-compose up

(Optional) Run without proxy:

docker-compose up --scale proxy=0

Run to create the required Fuseki datasets.

Different docker-compose files exist to accomodate different service configurations.

Custom certs

To use custom certificates for communication with this service's proxy, reference them in the proxy's Dockerfile. The gen-domain-cert script can be used to generate custom certs (e.g. 'maximoff.crt') using a CA root cert (e.g. 'consult.crt') and accompanying keys. If distributing an image outside of an organisation, edit Dockerfile and docker-compose to mount a volume on the host containing the certs instead, so that images are not transferred with the certs inside then.

Running the tests


Built With



Please read for details on our code of conduct, and the process for submitting pull requests to us.


We use SemVer for versioning. For the versions available, see the tags on this repository.


Produced as part of the CONSULT project.

CONSULT project


This project is licensed under the MIT License - see the file for details.


You can’t perform that action at this time.