A slightly opinionated, common set of 'rules' for creating rdf data using 'rdf-engine':
- Data Rules: for loading tables (csv), hierarchical (json), and rdf (ttl).
- Mapping rule: SPARQL construct
- Ontology rules: Inference and validation using Shifty
These rules come together in the 'engine'.
Development
Develop with uv sync --all-packages --all-extras.
Run python tasks.py stamp_ver before pypi publishing.
Design Choices
These are choices given the common use case of mapping data to an ontology. They are somewhat firm.
- RDF1.2 annotates tripes with metadata as
<<?s ?p ?o>> ?mp ?mowhere?mpand?mocorrespond to simple (key,value) pairs of metadata. - Mappings are in the form of SPARQL constructs
stored as files with a
.mapping.rqextension (can also be.mapping.sparql). - Each (specified) ontology will be processed separately.
- By default, no assumption about data identifier uniqueness is made:
A random unique identifier node,
with the default 'anon.id' prefix 'urn:rdf-rules:anon:id:',
will be created to identify data (table 'rows' or json data).
It is the user's responsibility to make 'nice' named nodes:
This can be by making a 'rule' to SPARQL
constructnodes or by specifyingjson2rdf_options={'subject_id_keys': {'id'}, id_prefix=(..., ...) }argument for Table or JSON data rules. With these options, nodes can be made unique over all data.
Usage
Examine how rules are fed into an engine in the tests. The common workflow is:
-
Specify rules. Each rule is constructed by providing arguments to
mkrule.mkruleprocesses objects such as file paths and data objects. A distinction is made for 'data rules'; These are only triggered once in the beginning. -
Specify engine run parameters. See
runfunction documentation. -
Extract data subsets with queries. 'System' queries are
mapped_and_inferredandvalidation(results). It's best to extract these using thequeryfunction as they slightly depend on configuration.
1 maintainer
Metadata
1 maintainer
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