AutoMATES: Automated Model Assembly from Text, Equations, and Software

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 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. An example of a model being represented in these three forms (text, equations, and software) is shown in Figure 1. This model is a differential equation describing the biophysical variable, leaf area index (LAI). The network on the right half of the figure is an aspirational representation of the model as a Bayesian network. Although this example is handcrafted, our end goal is to be able to automatically assemble models with this level of semantic richness.