Document RDFLib and pyld usage patterns

[edmondchuc]

We are collecting common use cases and patterns with working with JSON-LD in RDFLib and pyld and plan to add them to the RDFLib documentation.

The below details copied from this comment: #2308 (comment)

The pyld + RDFLib things I normally do:

Getting RDF into a framed JSON-LD

• Serialize an RDFLib graph as JSON-LD
• Alternatively, serialize the RDFLib graph as n-triples or n-quads and use the pyld processor' from_rdf function
• Use the frame function in the pyld processor with the JSON-LD data + frame and context as inputs

The above is the same for compaction and expansion by using the relevant function in the pyld processor.

Parsing JSON-LD with pyld and loading into an RDFLib graph

• To avoid some issues with the RDFLib's JSON-LD parser, we can use the pyld processor to parse it using the normalize function
• The result is an n-quads string. This can now be loaded into an RDFLib graph or dataset
• Alternatively, there's also a to_rdf function without the normalization algorithm. This is potentially faster.

[ajnelson-nist]

A usage¹ pattern I have is similar to your first heading, @edmondchuc . I have workflows that generate Git-tracked example data - most often in Turtle and/or JSON-LD. The steps I take are:

1. Serialize a graph as JSON-LD.
2. Use PyLD for compaction.

One example is in these Makefile lines, which call a Python wrapper to pyld.jsonld.compact here.

I wrote that logic a while before rdflib-jsonld was integrated into rdflib, and never got around to looking. Is there access to a compaction function in rdflib somewhere that obviates this usage pattern?

Footnotes

1. Participation by NIST in the creation of the documentation of mentioned software is not intended to imply a recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that any specific software is necessarily the best available for the purpose. ↩