Complete documentation available at ml4ai.github.io/delphi (the 'raw' version can be found in the docs directory.)

Modeling complex phenomena such as food insecurity requires reasoning over multiple levels of abstraction and fully utilizing expert knowledge about multiple disparate domains, ranging from the environmental to the sociopolitical.

Delphi is a Python library (3.6+) for assembling causal, dynamic, probabilistic models from information extracted from two sources:

• Text: Delphi utilizes causal relations extracted using machine reading from text sources such as UN agency reports, news articles, and technical papers.
• Software: Delphi also incorporates functionality to extract abstracted representations of scientific models from code that implements them, and convert these into probabilistic models.

Delphi builds upon INDRA and Eidos.

For a detailed description of our procedure to convert text to models, see this document.

Delphi is also part of the AutoMATES project.

If you use Delphi, please cite the following:

   @misc{Delphi,
       Author = {Adarsh Pyarelal and Paul Hein and Jon Stephens and Pratik
                 Bhandari and HeuiChan Lim and Saumya Debray and Clayton
                 Morrison},
       Title = {Delphi: A Framework for Assembling Causal Probabilistic 
                Models from Text and Software.},
       doi={10.5281/zenodo.1436915},
   }

Delphi is licensed under the Apache License 2.0.

The development of Delphi was supported by the Defense Advanced Research Projects Agency (DARPA) under the World Modelers (grant no. W911NF1810014) and Automated Scientific Knowledge Extraction (agreement no. HR00111990011) programs.