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Schema Annotations for Linked Avro Data (SALAD)

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Schema Salad

Salad is a schema language for describing JSON or YAML structured linked data documents. Salad schema describes rules for preprocessing, structural validation, and hyperlink checking for documents described by a Salad schema. Salad supports rich data modeling with inheritance, template specialization, object identifiers, object references, documentation generation, code generation, and transformation to RDF. Salad provides a bridge between document and record oriented data modeling and the Semantic Web.

The Schema Salad library is Python 3.6+ only.

Installation

pip3 install schema_salad

If you intend to use the schema-salad-tool –codegen=python feature, please include the [pycodegen] extra:

pip3 install schema_salad[pycodegen]

To install from source:

git clone https://github.com/common-workflow-language/schema_salad
cd schema_salad
pip3 install .
# or pip3 install .[pycodegen] if needed

Commands

Schema salad can be used as a command line tool or imported as a Python module:

$ schema-salad-tool
usage: schema-salad-tool [-h] [--rdf-serializer RDF_SERIALIZER] [--skip-schemas]
                      [--strict-foreign-properties] [--print-jsonld-context]
                      [--print-rdfs] [--print-avro] [--print-rdf] [--print-pre]
                      [--print-index] [--print-metadata] [--print-inheritance-dot]
                      [--print-fieldrefs-dot] [--codegen language] [--codegen-target CODEGEN_TARGET]
                      [--codegen-examples directory] [--codegen-package dotted.package]
                      [--codegen-copyright copyright_string] [--print-oneline]
                      [--print-doc] [--strict | --non-strict]
                      [--verbose | --quiet | --debug] [--only ONLY] [--redirect REDIRECT]
                      [--brand BRAND] [--brandlink BRANDLINK] [--brandstyle BRANDSTYLE]
                      [--brandinverse] [--primtype PRIMTYPE] [--version]
                      [schema] [document]

$ python
>>> import schema_salad

Validate a schema:

$ schema-salad-tool myschema.yml

Validate a document using a schema:

$ schema-salad-tool myschema.yml mydocument.yml

Generate HTML documentation:

$ schema-salad-tool --print-doc myschema.yml > myschema.html
$ # or
$ schema-salad-doc myschema.yml > myschema.html

Get JSON-LD context:

$ schema-salad-tool --print-jsonld-context myschema.yml mydocument.yml

Convert a document to JSON-LD:

$ schema-salad-tool --print-pre myschema.yml mydocument.yml > mydocument.jsonld

Generate Python classes for loading/generating documents described by the schema (Requires the [pycodegen] extra):

$ schema-salad-tool --codegen=python myschema.yml > myschema.py

Display inheritance relationship between classes as a graphviz ‘dot’ file and render as SVG:

$ schema-salad-tool --print-inheritance-dot myschema.yml | dot -Tsvg > myschema.svg

Codegen Examples

These are helpful to see how to use the output of schema-salad-tool –codegen in different languages for loading and/or creating/editing/saving objects, using the CWL v1.2 schema as an example.

Language

Repository

Serialization Example | Deserialization Example

Python

https://github.com/common-workflow-language/cwl-utils/

create_cwl_from_objects.py

load_document()

Java

https://github.com/common-workflow-language/cwljava/

(Not yet implemented)

PackedWorkflowClassTest.java

TypeScript

https://github.com/common-workflow-lab/cwl-ts-auto

Creating, editing, and saving CWL docs with TypeScript

Loading CWL documents with TypeScript

.Net

https://github.com/common-workflow-lab/CWLDotNet

Creating, editing, and saving CWL docs with .Net

Loading CWL documents with .Net

C++

https://github.com/common-workflow-lab/cwl-cpp-auto

cwl_output_example.cpp

(Not yet implemented)

D

https://github.com/common-workflow-lab/cwl-d-auto

How to use

How to use

Quick Start

Let’s say you have a ‘basket’ record that can contain items measured either by weight or by count. Here’s an example:

basket:
  - product: bananas
    price: 0.39
    per: pound
    weight: 1
  - product: cucumbers
    price: 0.79
    per: item
    count: 3

We want to validate that all the expected fields are present, the measurement is known, and that “count” cannot be a fractional value. Here is an example schema to do that:

- name: Product
  doc: |
    The base type for a product.  This is an abstract type, so it
    can't be used directly, but can be used to define other types.
  type: record
  abstract: true
  fields:
    product: string
    price: float

- name: ByWeight
  doc: |
    A product, sold by weight.  Products may be sold by pound or by
    kilogram.  Weights may be fractional.
  type: record
  extends: Product
  fields:
    per:
      type:
        type: enum
        symbols:
          - pound
          - kilogram
      jsonldPredicate: '#per'
    weight: float

- name: ByCount
  doc: |
    A product, sold by count.  The count must be a integer value.
  type: record
  extends: Product
  fields:
    per:
      type:
        type: enum
        symbols:
          - item
      jsonldPredicate: '#per'
    count: int

- name: Basket
  doc: |
    A basket of products.  The 'documentRoot' field indicates it is a
    valid starting point for a document.  The 'basket' field will
    validate subtypes of 'Product' (ByWeight and ByCount).
  type: record
  documentRoot: true
  fields:
    basket:
      type:
        type: array
        items: Product

You can check the schema and document in schema_salad/tests/basket_schema.yml and schema_salad/tests/basket.yml:

$ schema-salad-tool basket_schema.yml basket.yml
Document `basket.yml` is valid

Documentation

See the specification and the metaschema (salad schema for itself). For an example application of Schema Salad see the Common Workflow Language.

Rationale

The JSON data model is an popular way to represent structured data. It is attractive because of it’s relative simplicity and is a natural fit with the standard types of many programming languages. However, this simplicity comes at the cost that basic JSON lacks expressive features useful for working with complex data structures and document formats, such as schemas, object references, and namespaces.

JSON-LD is a W3C standard providing a way to describe how to interpret a JSON document as Linked Data by means of a “context”. JSON-LD provides a powerful solution for representing object references and namespaces in JSON based on standard web URIs, but is not itself a schema language. Without a schema providing a well defined structure, it is difficult to process an arbitrary JSON-LD document as idiomatic JSON because there are many ways to express the same data that are logically equivalent but structurally distinct.

Several schema languages exist for describing and validating JSON data, such as JSON Schema and Apache Avro data serialization system, however none understand linked data. As a result, to fully take advantage of JSON-LD to build the next generation of linked data applications, one must maintain separate JSON schema, JSON-LD context, RDF schema, and human documentation, despite significant overlap of content and obvious need for these documents to stay synchronized.

Schema Salad is designed to address this gap. It provides a schema language and processing rules for describing structured JSON content permitting URI resolution and strict document validation. The schema language supports linked data through annotations that describe the linked data interpretation of the content, enables generation of JSON-LD context and RDF schema, and production of RDF triples by applying the JSON-LD context. The schema language also provides for robust support of inline documentation.

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SHA256 46e1f334fd51f09e500ff4fe80c68fb5497bc293742ac599d9c40c6ee2baf82d
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SHA256 5a7e84f7f6a5e7d97b55a342d81e2969014a1b135bba749479b694c0264e043d
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SHA256 a6c0c26d12f9081d65621e491708f3c75d8f7ffe0877f980c90c34e779d45d6c
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SHA256 6789d1d40066c527b8710c69f17b62f485ef92762ea35ba50334b9fe5c1b5c6e
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SHA256 441bc6b2c868e9e8d158b6392fda5c87d1adaf508aa43b101fa7bd6d2dd97eba
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SHA256 aa35788cdf38becde9166d1703622176f30a1dc62b71fc7b654faea30fb2c3be
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SHA256 2abe3c6c0308257d5ee9a2755094c60983e5ce9ff1a0594b74d83f982b434ebf
MD5 d8c7e8042a39e288618bdafa8b056588
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SHA256 ea0d4c3942317ad3f9a2c0c9dd9ed8d74752b67736721db07a267dab301d1d6c
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SHA256 2c0c9f3112627b01bf1eba7633de4d191f0e7dc617604228f810379a65cfc6cd
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SHA256 c55f5ecb7fa1fe844fbdb50f4400a7339f12d053394c51955263541a5ead3cd7
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SHA256 1baf114f9128fdbe89f816f7abf93cd2a7927e3e9fc30633ec741badcfe475d4
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SHA256 6f13c693dbd954d36696f42c7eeb03755ffe1ffdc1e778010d55786671c332b5
MD5 eba553b621ae950cdfe9892d208438da
BLAKE2b-256 08ffdde10911bf7fb03dd5f1620ece8918509513be1eb3b84a17e382e269c27d

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SHA256 85cdf3850f1215a3c741d729c1f65514f2ba90dec3ee3597a3a44ac7cb2657e0
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SHA256 7be63416d551ac8e4eb6ecc83268a6ce1775a363f965c60957a27083acff8d96
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SHA256 639fe4c9ca2fa3f577ae6661a4a538ff10d76a7a85c309d480dc278e0ddd635d
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SHA256 eb1581920d96c8044376a072cb45189a5aac743789d9b203fe5351a5d1ab7d58
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SHA256 d0006e17d5c2d3edb5f6dc7e915a2355d4ec22a686960f0eecb22bba9c40acfc
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SHA256 2c4550c83760138a242010bd75db589104f19c9071b4edb805dd101137097c70
MD5 433c8ebd09527504938ac91e7e2f0944
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SHA256 4e88d3f46895a15c3356840a84cfc965a60ffa16233d3a7237425c294fb08975
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SHA256 a496464826b187b6a4a0da14ab9754940e6310a35ba5f22b37de411f4267ae9f
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SHA256 dc8fc3f9b25305a2bd660d3343ec099a4d620a659b62530a543f5f5aa30939a2
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SHA256 217aff85c2eac567dd490fd3c5fc44f0c6323e5c772ab04c599396e05e556f67
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