PyLD

Introduction

This library is an implementation of the JSON-LD specification in Python.

JSON, as specified in RFC7159, is a simple language for representing objects on the Web. Linked Data is a way of describing content across different documents or Web sites. Web resources are described using IRIs, and typically are dereferencable entities that may be used to find more information, creating a "Web of Knowledge". JSON-LD is intended to be a simple publishing method for expressing not only Linked Data in JSON, but for adding semantics to existing JSON.

JSON-LD is designed as a light-weight syntax that can be used to express Linked Data. It is primarily intended to be a way to express Linked Data in JavaScript and other Web-based programming environments. It is also useful when building interoperable Web Services and when storing Linked Data in JSON-based document storage engines. It is practical and designed to be as simple as possible, utilizing the large number of JSON parsers and existing code that is in use today. It is designed to be able to express key-value pairs, RDF data, RDFa data, Microformats data, and Microdata. That is, it supports every major Web-based structured data model in use today.

The syntax does not require many applications to change their JSON, but easily add meaning by adding context in a way that is either in-band or out-of-band. The syntax is designed to not disturb already deployed systems running on JSON, but provide a smooth migration path from JSON to JSON with added semantics. Finally, the format is intended to be fast to parse, fast to generate, stream-based and document-based processing compatible, and require a very small memory footprint in order to operate.

Requirements

• Python (2.7 or later)

Installation

PyLD can be installed with pip:

pip install PyLD

Quick Examples

from pyld import jsonld
import json

doc = {
    "http://schema.org/name": "Manu Sporny",
    "http://schema.org/url": {"@id": "http://manu.sporny.org/"},
    "http://schema.org/image": {"@id": "http://manu.sporny.org/images/manu.png"}
}

context = {
    "name": "http://schema.org/name",
    "homepage": {"@id": "http://schema.org/url", "@type": "@id"},
    "image": {"@id": "http://schema.org/image", "@type": "@id"}
}

# compact a document according to a particular context
# see: http://json-ld.org/spec/latest/json-ld/#compacted-document-form
compacted = jsonld.compact(doc, context)

print(json.dumps(compacted, indent=2))
# Output:
# {
#   "@context": {...},
#   "image": "http://manu.sporny.org/images/manu.png",
#   "homepage": "http://manu.sporny.org/",
#   "name": "Manu Sporny"
# }

# compact using URLs
jsonld.compact('http://example.org/doc', 'http://example.org/context')

# expand a document, removing its context
# see: http://json-ld.org/spec/latest/json-ld/#expanded-document-form
expanded = jsonld.expand(compacted)

print(json.dumps(expanded, indent=2))
# Output:
# [{
#   "http://schema.org/image": [{"@id": "http://manu.sporny.org/images/manu.png"}],
#   "http://schema.org/name": [{"@value": "Manu Sporny"}],
#   "http://schema.org/url": [{"@id": "http://manu.sporny.org/"}]
# }]

# expand using URLs
jsonld.expand('http://example.org/doc')

# flatten a document
# see: http://json-ld.org/spec/latest/json-ld/#flattened-document-form
flattened = jsonld.flatten(doc)
# all deep-level trees flattened to the top-level

# frame a document
# see: http://json-ld.org/spec/latest/json-ld-framing/#introduction
framed = jsonld.frame(doc, frame)
# document transformed into a particular tree structure per the given frame

# normalize a document
normalized = jsonld.normalize(doc, {'format': 'application/nquads'})
# normalized is a string that is a canonical representation of the document
# that can be used for hashing

Commercial Support

Commercial support for this library is available upon request from Digital Bazaar: support@digitalbazaar.com.

Source

The source code for the Python implementation of the JSON-LD API is available at:

http://github.com/digitalbazaar/pyld

Tests

This library includes a sample testing utility which may be used to verify that changes to the processor maintain the correct output.

To run the sample tests you will need to get the test suite files by cloning the json-ld.org repository hosted on GitHub:

https://github.com/json-ld/json-ld.org

Then run the test application using the directory containing the tests:

python tests/runtests.py -d {PATH_TO_JSON_LD_ORG/test-suite}