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The scientific literature is a rich source of information for data mining with conceptual knowledge graphs; the open science movement has enriched this literature with complementary source code that implement their respective scientific models. To exploit this new resource, we construct a knowledge graph using unsupervised learning methods to identify conceptual entities. We associate source code entities to these natural language concepts using expert rules. Practical naming conventions for methods and functions tend to reflect the concept they implement.

SemanticModels.jl is a system for extracting semantic information from scientific code and reconciling it with conceptual descriptions to build a knowledge graph. This knowledge graph represents the connections between elements of code (variables, values, functions, and expressions) and elements of scientific understanding (concepts, terms, relations), and can be reasoned over to facilitate several metamodeling tasks, including model augmentation, synthesis, and validation. We present a category theory-based framework for defining metamodeling tasks and extracting semantic information from model implementations, and show how SemanticModels.