Skip to main content

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 ?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:

  1. 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.

  2. Specify engine run parameters. See run function documentation.

  3. 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

Release files for rdf-rules 2026.9.76

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for rdf-rules 2026.9.76
File Size Uploaded
rdf_rules-2026.9.76.tar.gz 9.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for rdf-rules 2026.9.76
File Interpreter ABI Platform
rdf_rules-2026.9.76-py3-none-any.whl Python 3 none any Details

Total release size: 23.5 kB

Release files / rdf_rules-2026.9.76.tar.gz

Download URL rdf_rules-2026.9.76.tar.gz
Size 9.2 kB
Tags Source
SHA-256 checksum
How to use checksums
07ee878a535d36291c76ca010ccac0e27cfb04ff9e3e231ebd5c770198f4fe3b
BLAKE2b-256 checksum
How to use checksums
0d3c93d9cfe2daf870e0ea8183cb033be32f14b66212110e70239a9c4bd31168
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 16, 2026.

Transparency log

Release files / rdf_rules-2026.9.76-py3-none-any.whl

Download URL rdf_rules-2026.9.76-py3-none-any.whl
Size 14.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
c0982c515bd2ce0a9dd8c4a3b1dc24b2a1b3db24be1b1198594444ea8b9af39e
BLAKE2b-256 checksum
How to use checksums
fbd044693d2002c03b65a3869049f97354c0fda023c20ad200b9e38fabcb3d62
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 16, 2026.

Transparency log
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page