ExpO: Towards Explaining Ontology-Driven Conceptual Models

Expose: ExpO API Server

An API service for performing transformation operations over Ontology Driven Conceptual Models.

The server is running here.

If you are interested to know more, feel free to open an issue to provide feedback on the project or reach our team members for more specific cases:

• Elena Romanenko
• Diego Calvanese
• Giancarlo Guizzardi

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Endpoints

Endpoint documentation is available at /docs and /redoc endpoints

Examples of requests

Test if the server is running and accepts requests:

[GET] http://host-name:port/health

Ask for 2 definitions (apply define) to the concept 'Mother'

[GET] https://host-name:port/define?concept=Mother&number_of_def=2

Apply abstraction (parthood and hierarchy) to the model

[POST] http://host-name:port/abstract

with the following body:

{
  "abs_type": [
    "parthood",
    "hierarchy"
  ],
  "long_names": true,
  "mult_relations": false,
  "keep_relators": true,
  "in_format": "json",
  "out_format": "json",
  "origin": {
    PLACE_HERE_YOUR_ODCM
  }
}

As an example of the ondology-driven conceptual model you may take any model from the OntoUML/UFO Catalog.

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If you want to run your own server

Requirements

• Docker 20.10 or later
• GitHub account

Installation

In general, you need only to run the docker-compose command. However, if you want to run expand operation, you will need to specify your credentials in .env.example.

git clone [email protected]:mozzherina/Expose.git
cp .env.example .env
docker-compose --compatibility up -d

Specifying credentials for GitHub

1. Create a fork from the repository.
2. Go to your GitHub account and create an access token for this repository: Your GitHub Profile / Settings / Developer Settings / Fine-grained Tokens / Generate new token.
3. In env.example change GIT_USER and GIT_TOKEN to your own values.

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If you want to cite this work

Please, refer to the PURL and cite the paper:

Romanenko, E., Calvanese, D., Guizzardi, G.: ExpO: Towards Explaining Ontology-Driven Conceptual Models. (2024) DOI