Canonicalization & Diff

Byte-deterministic serialization (Codecs & Determinism) means the same dataset always emits the same bytes. Canonicalization is the stronger property: two different in-memory datasets that are isomorphic — the same graph up to blank-node relabeling — canonicalize to the same bytes.

RDFC-1.0

PurRDF implements W3C RDF Dataset Canonicalization (RDFC-1.0) natively in the kernel, tested against the W3C rdf-canon fixture suite (65 vectors — 64 eval plus 1 negative — all green; see docs/CONFORMANCE.md).

The entry point is canonicalize (with a canonicalize_with variant for choosing the hash), producing canonical blank-node labels and, one layer up in purrdf-rdf, canonical flat N-Quads over the frozen IR:

use purrdf::canonicalize;

let canon = canonicalize(&ds);
// Canonical labels are stable across runs, hosts, and language bindings.

Use canonicalization when you need a content identity for a graph: hashing, signing, deduplication, or comparing datasets produced by different writers.

Isomorphism

datasets_isomorphic(a, b) decides whether two frozen datasets are RDF-structurally isomorphic: the same quads under a blank-node bijection. Canonicalization gives the equivalent verdict — two datasets are isomorphic iff their canonicalizations are equal — but the direct check is the convenient form for tests and harnesses. PurRDF’s own conformance harnesses use RDFC-1.0 isomorphism to compare, for example, SHACL Rules output graphs against expected inferred graphs.

Diff

dataset_diff(a, b) produces a structural diff between two frozen datasets, including an isomorphic verdict. For a human-facing review flow, purrdf-rdf additionally provides per-subject Symmetric-CBD extraction (“describe”) and a review-friendly Turtle normalizer, so a graph change reads like a code change.

Choosing the right tool

API details are on docs.rs/purrdf-core (the canonicalize, datasets_isomorphic, and dataset_diff items) and docs.rs/purrdf-rdf (describe and normalization).