There exist today state-of-the-art computational models that can provide highly accurate predictions about complex phenomena such as crop growth and weather patterns. However, certain phenomena, such as food insecurity, involve a host of factors that cannot be modeled by any single one of these models, but which instead require the integration of multiple models.
To truly integrate these computational models, it is necessary to ‘lift’ them to a common representation that is (i) agnostic to the software implementation, (ii) semantically rich enough to represent the implicit domain knowledge in the models, and (iii) connected to scientific concepts, as expressed in the domain literature.
The AutoMATES project aims to build technology to construct and curate semantically-rich representations of scientific models by integrating three different sources of information:
- natural language descriptions of models in publications and other technical documentation,
- the equations contained in these documents,
- and the software the implements these models.
In the figure below, a dynamical model describing the biophysical variable Leaf Area Index (LAI) is described using text, equations, and software (left half of the figure). The network on the right half is an aspirational representation of the model as a Bayes network. Although this example is hand-crafted, our end goal is to be able to automatically assemble models with this level of semantic richness.
This work will dramatically advance the state-of-the-art in automated model curation and integration, enabling scientists and analysts to understand complex mechanisms that span multiple domains. By exposing the implicit domain knowledge baked into computational models, this effort will enable automated model composition and reasoning in context to directly support the development of ‘third wave’ artificial intelligence.
For more information, see our paper from the Modeling the World’s Systems 2019 conference (arXiv)