Paper/Poster/Presentation Title
Keywords
Software registry; software metadata; model integration; model description
Start Date
26-6-2018 10:40 AM
End Date
6-2018 12:00 PM
Abstract
OBJECTIVES: Model repositories are key resources for scientists in terms of model discovery and reuse, but do not focus on important tasks such as model comparison and composition. Model repositories do not typically capture important comparative metadata to describe assumptions and model variables that enable a scientist to discern which models would be better for their purposes. Furthermore, once a scientist selects a model from a repository it takes significant effort to understand and use the model. Our goal is to develop model repositories with machine-actionable model metadata that can be used to provide intelligent assistance to scientists in model selection and reuse.
METHODOLOGY: We are extending the OntoSoft semantic software metadata registry (http://www.ontosoft.org/) to include machine-readable metadata. This work includes: 1) exposing model variables and their relationships; 2) adopting a standardized representation of model variables based on the conventions of the Geoscience Standard Names ontology (GSN) (http://www.geoscienceontology.org/); 3) capturing the semantic structure of model invocation signatures based on functional inputs and outputs and their correspondence to model variables; 4) associating models with readily reusable workflow fragments for data preparation, model calibration, and visualization of results.
FINDINGS: We have extended OntoSoft to expose model variables and adopt GSN ontologies to describe hydrology models. We are designing representations to capture the semantic structure of model invocation signatures that maps model variables to data requirements to facilitate discovery and comparison of models.
SIGNIFICANCE: The extended OntoSoft framework would reduce the time to find, understand, compare and reuse models.
A Semantic Model Catalog to Support Comparison and Reuse
OBJECTIVES: Model repositories are key resources for scientists in terms of model discovery and reuse, but do not focus on important tasks such as model comparison and composition. Model repositories do not typically capture important comparative metadata to describe assumptions and model variables that enable a scientist to discern which models would be better for their purposes. Furthermore, once a scientist selects a model from a repository it takes significant effort to understand and use the model. Our goal is to develop model repositories with machine-actionable model metadata that can be used to provide intelligent assistance to scientists in model selection and reuse.
METHODOLOGY: We are extending the OntoSoft semantic software metadata registry (http://www.ontosoft.org/) to include machine-readable metadata. This work includes: 1) exposing model variables and their relationships; 2) adopting a standardized representation of model variables based on the conventions of the Geoscience Standard Names ontology (GSN) (http://www.geoscienceontology.org/); 3) capturing the semantic structure of model invocation signatures based on functional inputs and outputs and their correspondence to model variables; 4) associating models with readily reusable workflow fragments for data preparation, model calibration, and visualization of results.
FINDINGS: We have extended OntoSoft to expose model variables and adopt GSN ontologies to describe hydrology models. We are designing representations to capture the semantic structure of model invocation signatures that maps model variables to data requirements to facilitate discovery and comparison of models.
SIGNIFICANCE: The extended OntoSoft framework would reduce the time to find, understand, compare and reuse models.
Stream and Session
A4: Model Integration Frameworks: A Discussion of Typologies, Standards, Languages, and Platforms