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Update algorithm output to be the serialized canonical form #97

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May 5, 2023
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Update algorithm output to be the serialized canonical form #97

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8da4f0e
Update definition of "normalized dataset" to be an RDF dataset and an…
gkellogg May 2, 2023
5a33070
Update spec/index.html
gkellogg May 3, 2023
382823d
The map component of a normalized dataset is now unordered and relate…
gkellogg May 3, 2023
fa8ecd5
Remove redundant sentence in definition of a normalized dataset.
gkellogg May 5, 2023
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34 changes: 19 additions & 15 deletions spec/index.html
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Expand Up @@ -296,11 +296,10 @@ <h3>Terms defined by this specification</h3>
<dd>The abstract <a>RDF dataset</a> that is provided as input to
the algorithm.</dd>
<dt><dfn>normalized dataset</dfn></dt>
<dd>The immutable, abstract <a>RDF dataset</a> and a set of normalized
<a>blank node identifiers</a> that are produced as output by the algorithm.
A <a>normalized dataset</a> is a restriction on an <a>RDF dataset</a>
where all nodes are labeled, and <a>blank nodes</a> are labeled with canonical <a>blank node identifiers</a>
consistent with running this algorithm on a base <a>RDF dataset</a>.
<dd>A <a>normalized dataset</a> is the combination of an <a>RDF dataset</a>
and a <a>map</a> where [=map/keys=]
are <a>blank nodes</a> from the dataset
and [=map/values=] are the associated canonical <a>blank node identifiers</a>.
A concrete serialization of a <a>normalized dataset</a> MUST label
all <a>blank nodes</a> using these stable <a>blank node identifiers</a>.</dd>
<dt><dfn>identifier issuer</dfn></dt>
Expand Down Expand Up @@ -409,16 +408,19 @@ <h3>Terms defined by cited specifications</h3>
<h2>Canonicalization</h2>

<p>Canonicalization is the process of transforming an
<a>input dataset</a> to a <a>normalized dataset</a>. That
is, any two <a>input datasets</a> that contain the same
information, regardless of their arrangement, will be transformed into
identical <a>normalized dataset</a>. The problem requires directed
<a>input dataset</a> to its <a>serialized canonical form</a>.
That is, any two <a>input datasets</a> that contain the same information,
regardless of their arrangement,
will be transformed into the same <a>serialized canonical form</a>.
The problem requires directed
graphs to be deterministically ordered into sets of nodes and edges. This
is easy to do when all of the nodes have globally-unique identifiers, but
can be difficult to do when some of the nodes do not. Any nodes without
globally-unique identifiers must be issued deterministic identifiers.</p>

<p class="ednote">Strictly speaking, the normalized dataset must be serialized to be stable, as within a dataset, blank node identifiers have no meaning. This specification defines a <a>normalized dataset</a> to include stable identifiers for blank nodes, but practical uses of this will always generate a canonical serialization of such a dataset.</p>
<p class="note">
This specification defines a <a>normalized dataset</a> to include stable identifiers for blank nodes,
practical uses of which will always generate a canonical serialization of such a dataset.</p>

<p>In time, there may be more than one canonicalization algorithm and,
therefore, for identification purposes, this algorithm is named the
Expand Down Expand Up @@ -523,7 +525,6 @@ <h2>Blank Node Identifier Issuer State</h2>
<h2>Canonicalization Algorithm</h2>

<p class="ednote">At the time of writing, there are several open issues that will determine important details of the canonicalization algorithm.</p>
<div class="issue" data-number="4"></div>
<div class="issue" data-number="7"></div>
<div class="issue" data-number="8"></div>
<div class="issue" data-number="10"></div>
Expand Down Expand Up @@ -576,7 +577,8 @@ <h3>Overview</h3>
<a href="#issue-identifier" class="sectionRef"></a>.
If more than one node produces the same N-degree hash,
the order in which these nodes receive a canonical identifier does not matter.</li>
<li id="ca-hl.6"><strong>Finish</strong>. Return the normalized dataset.</li>
<li id="ca-hl.6"><strong>Finish</strong>.
Return the <a>serialized canonical form</a> of the <a>normalized dataset</a>.</li>
</ol>
</section>

Expand Down Expand Up @@ -1252,7 +1254,8 @@ <h3>Algorithm</h3>
</pre>
</details>
</li>
<li id="ca.7">Return the <a>normalized dataset</a>.</li>
<li id="ca.7">Return the <a>serialized canonical form</a>
of the <a>normalized dataset</a>.</li>
</ol>
</section>
</section>
Expand Down Expand Up @@ -2630,7 +2633,7 @@ <h2>Serialization</h2>
<p>This section describes the process of creating a serialized [[N-Quads]] representation
of a <a>normalized dataset</a>.</p>

<p>The <dfn class="lint-ignore">serialized canonical form</dfn> of a <a>normalized dataset</a>
<p>The <dfn>serialized canonical form</dfn> of a <a>normalized dataset</a>
is an N-Quads document [[N-QUADS]]
created by representing each <a>quad</a> from the <a>normalized dataset</a>
in <a>canonical n-quads form</a>,
Expand All @@ -2643,7 +2646,8 @@ <h2>Serialization</h2>

<p>When serializing quads in <a>canonical n-quads form</a>,
components which are <a>blank nodes</a> MUST be serialized using the
canonical label associated with each <a>blank node</a> in the <a>normalized dataset</a>.</p>
canonical label associated with each <a>blank node</a>
from the <a>map</a> component of the <a>normalized dataset</a>.</p>

<aside id="ex-ser-unique-hashes"
class="example"
Expand Down