A slightly opinionated, common set of 'rules' for creating rdf data using 'rdf-engine':

Data Rules: for loading tables (csv, parquet), hierarchical (json), and rdf (ttl).
Mapping rule: SPARQL construct
Ontology rules: Inference and validation using Shifty

These rules come together in the 'engine'.

Development

History/context: This framework is a generalization of BIM2RDF.

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 ?mo where ?mp and ?mo correspond to simple (key,value) pairs of metadata.
Mappings are in the form of SPARQL constructs stored as files with a .mapping.rq extension (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🆔', 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 construct nodes or by specifying json2rdf_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. mkrule processes 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 run function documentation.
Extract data subsets with queries. 'System' queries are mapped_and_inferred and validation (results). It's best to extract these using the query function as they slightly depend on configuration.

Metadata

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