https://github.com/openschemas/schemaorg
python functions for applied use of schema.org
https://github.com/openschemas/schemaorg
curation data schemaorg software
Last synced: 4 months ago
JSON representation
python functions for applied use of schema.org
- Host: GitHub
- URL: https://github.com/openschemas/schemaorg
- Owner: openschemas
- License: mpl-2.0
- Created: 2018-10-21T03:55:18.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-11-15T20:41:39.000Z (over 4 years ago)
- Last Synced: 2025-10-21T19:44:14.058Z (8 months ago)
- Topics: curation, data, schemaorg, software
- Language: HTML
- Homepage: https://openschemas.github.io/schemaorg/
- Size: 11.7 MB
- Stars: 36
- Watchers: 4
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: .github/CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# schemaorg Python
This module will serve functions for researchers and users to apply schema.org
definitions to their datasets, software, and other worldly things. For
development functions with Python along with the web interface that
serves the published specifications, please see [https://www.github.com/schemaorg/schemaorg](https://www.github.com/schemaorg/schemaorg).
For versions 0.1.0 and later, the structure of the data files changed, so we provide
schema.org 12.0 and later. For older versions of schemaorg, use versions prior
to 12.0.
## What is this not?
This library is not intended to provide advanced functions around querying the ontology,
but rather accessing the definitions and tagging content with them.
## What is this for?
Please reference the [development](https://openschemas.github.io/schemaorg/) documentation
to read about the intended use cases that we are working on.
## Organization
Generally, we are extracting metadata from datasets and software, and then shoving
that metadata into a specification from schema.org. The final thing that we produce
is likely to be some form of json (e.g., json-ld) that can be embedded in a web
page or similar, intended to power search. Since the specific needs of a particular
webby place may vary, along with a data type we are extracting, the module provides
"templates" in the [templates](schemaorg/templates) directory. Specifically:
- The subfolders represent different use cases. For example, [templates/google](schemaorg/templates/google) has a simple html template, and a requirements file (called a recipe) to produce a Dataset and SoftwareSourceCode.
- Each subfolder, in an optional `__init__.py` file, also contains helper functions toward this goal. For example, the same google subfolder has such a file with a `make_person` function that users can quickly use to generate a person object.
- For each corresponding subfolder, there is generally an example in the [examples](examples) folder that also helps to see how it works.