Towards Improving Automatic Composition of Scientific Workflows: Variable Reconciliation and Tie-Breaking Strategy

The incorporation of computational workflows is well documented in disciplines like astrophysics, medicine, and environmental sciences. Their ability to represent computational components and data interactions enables automated scientific workflows, leading to increased efficiency and reproducibility. Diverse approaches have been proposed to improve the experience of crafting scientific experiments into computational workflows where manual and semi-automatic methods are well documented. In previous work, we proposed a method for automating workflow composition. However, component-to-component integration remains a challenge as common metadata notations are not enough to automatically identify equivalent variables under implicit scientific assumptions. In this work, we present VaR-O, an ontology to semantically identify equivalent variables based on implicit design decisions taken by domain experts when building scientific components. We also implement a tie-breaking strategy for component selection during the automated workflow composition process.