Ontological description of the relations in the causalgraph package
Detailed ontology documentation
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The causalgraph ontology defines the causal data model for the causalgraph package. The ontology defines essential components of a causal graph, like nodes and edges, in an RDF-compatible way. Edges are modeled as separate Resources to allow the addition of detailed information, like time lag for time series or confidence in the edge's existence, if learned by causal discovery algorithms.
For custom use cases, the ontology is extendable by inheritance from the base components. Custom ontologies that use or inherit from the causalgraph-ontolgy components will, upon import in causalgraph, automatically be detected as causal information and are thus directly available for import, export, and visualization capabilities of causalgraph.
A detailed description of the ontology including its classes, properties and axioms can be found at our GitHub page.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
- Mail: causalgraph@iwu.fraunhofer.de
- Blog: https://www.kognitive-produktion.de/?p=3154
- Project Link: https://github.com/causalgraph
The development of causalgraph is part of the research project KausaLAssist. It is funded by the German Federal Ministry of Education and Research (BMBF) within the "Future of Value Creation - Research on Production, Services and Work" program (funding number 02P20A150) and managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.
If you find causalgraph useful for your research, please cite us as follows:
Sven Pieper, Carl Willy Mehling, Dominik Hirsch, Tobias Lüke, Steffen Ihlenfeldt. causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs. 2023. https://arxiv.org/abs/2301.08490
DOI: 10.48550/arXiv.2301.08490
Bibtex:
@misc{
doi = {10.48550/ARXIV.2301.08490},
url = {https://arxiv.org/abs/2301.08490},
author = {Pieper, Sven and Mehling, Carl Willy and Hirsch, Dominik and Lüke, Tobias and Ihlenfeldt, Steffen},
keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences, E.1; E.2},
title = {causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs},
publisher = {arXiv},
year = {2023}
}