rdfjs-c14n

RDF Canonicalization in TypeScript

This is an implementation (under development) of the RDF Dataset Canonicalization algorithm. (The algorithm is being specified by the W3C RDF Dataset Canonicalization and Hash Working Group)

The specification is not yet final. This implementations aims at reflecting exactly the specification, which means it may evolve alongside the specification.

Requirements

The implementation depends on the interfaces defined by the RDF/JS Data model specification for RDF terms, named and blank nodes, or quads. It also depends on an instance of an RDF Data Factory, specified by the aforementioned specification. For TypeScript, the necessary type specifications are available through the @rdfjs/types package; an implementation of the RDF Data Factory is provided by, for example, the n3 package (but there are others), which also provides a Turtle/TriG parser and serializer to test the library.

By default (i.e., if not explicitly specified) the Data Factory of the n3 package is used.

An input RDF Dataset may be represented by:

• A Set of Quad instances; or
• An Array of Quad instances; or
• An instance of a Core Dataset

The canonicalization process returns

• A Set or an Array of Quad instances, if the input was a Set or an Array, respectively;
• An instance of a Core Dataset if the input was a Core Dataset and the canonicalization is initialized with an instance of the Dataset Core Factory; otherwise it is a Set of Quad instances.

The separate testing folder includes a tiny application that runs the official specification tests, and can be used as an example for the additional packages that are required.

Installation

The usual npm installation can be used:

npm rdf-c14n

The package has been written in TypeScript but is distributed in JavaScript; the type definition (i.e., index.d.ts) is included in the distribution.

Usage

There is a more detailed documentation of the classes and types on github. The basic usage may be as follows:

import * as n3  from 'n3';
import * as rdf from 'rdf-js';
// The definition of "Quads" is:
// export type Quads = rdf.DatasetCore<rdf.Quad,rdf.Quad> | rdf.Quad[] | Set<rdf.Quad>; 
import {RDFCanon, Quads, quadsToNquads } from 'rdf-c14n';

main() {
    // Any implementation of the data factory will do in the call below.
    // By default, the Data Factory of the `n3` package (i.e., the argument in the call
    // below is not strictly necessary).
    // Optionally, an instance of a Dataset Core Factory may be added as a second argument.
    const canonicalizer = new RDFCanon(n3.DataFactory);  

    const input = parseYourFavoriteTriGIntoQuads();

    // "normalized" is a dataset of quads with "canonical" blank node labels
    // per the specification 
    const normalized: Quads = canonicalizer.canonicalize(input)

    // "hash" is the hash value of the canonical dataset, per specification
    const hash = canonicalizer.hash(normalized);

    // Generate a sorted array of nquads for the normalized dataset
    const nquads = canonicalizer.toNquads(normalized);
}

Additional features

Choice of hash

The RDF Dataset Canonicalization algorithm is based on an extensive usage of hashing. By default, as specified by the document, the hash function is 'sha256'. This default hash function can be changed via the

    canonicalizer.setHashAlgorithm(algorithm);

method, where algorithm can be any hash function identification that the underlying openssl environment (as used by node.js) accepts. Examples are 'sha256', 'sha512', etc. On recent releases of OpenSSL, openssl list -digest-algorithms will display the available algorithms.

Logging

The canonicalization algorithm has built-in logging points that can be followed via a logger. This is only of interest for debugging the algorithm itself; it can be safely ignored by the average user. By default, no logging happens.

A built-in logger can be switched on which displays logging information in YAML. To use this YAML logger, do the following:

import { YamlLogger, LogLevels } from 'rdf-c14n';

main() {
    …
    const canonicalizer = new RDFCanon(n3.DataFactory);
    // `logLevel` may be LogLevels.error, LogLevels.warn, LogLevels.info, LogLevels.debug  
    const logger = new YamlLogger(logLevel);
    canonicalizer.setLogger(logger);
    …
    // "logger.log" is a string containing the full log in YAML format
    console.log(logger.log);
}

See the interface specification for Logger to possibly implement your own logger.

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Maintainer: @iherman

Changelog

Version 1.0.2

• First version of the documentation completed