Support for semantic annotations in the captured provenance using ontologies #26
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added 22 commits
March 8, 2023 15:28
…rs and returns. Implemented unit tests.
Implemented unit tests.
…pplications). OntologyInformation interface class made private. Updated and completed unit tests.
# Conflicts: # alpaca/decorator.py # alpaca/serialization/prov.py
…on returns that are containers and are captured with the container_output option in Provenance
… to the function with an ontology (regular inputs and container inputs)
Unit tests and recursive code refactored.
added 4 commits
November 21, 2023 13:55
# Conflicts: # alpaca/data_information.py # alpaca/serialization/prov.py
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This PR adds the functionality to extend the captured provenance with semantic information defined in one or more ontologies.
For example, function executions are represented by the
FunctionExecutionclass in the Alpaca ontology. However, the concept that represents the process implemented by the function could be defined in a domain-specific ontology, and this class could be added as an additionalrdf:typetriple to theFunctionExecutionindividual, thereby embedding semantic information.The ontology annotations are defined through a dictionary that is added as a special attribute to Python objects (
__ontology__). Two types of Python objects are supported: functions and data objects (i.e., objects instantiated from a Python class). Alpaca will detect the presence of the ontology annotations and use them appropriately when creating the serialized RDF graph.The ontology annotations can describe the function or data object as well as extended information associated to them:
The references to ontology classes/individuals must be URIs. CURIEs (i.e., abbreviated URIs that use a prefix) are allowed by defining namespaces.
More details are on the documentation page.