RDF Dataset Canonicalization

Abstract

RDF [RDF11-CONCEPTS] describes a graph-based data model for making claims about the world and provides the foundation for reasoning upon that graph of information. At times, it becomes necessary to compare the differences between sets of graphs, digitally sign them, or generate short identifiers for graphs via hashing algorithms. This document outlines an algorithm for normalizing RDF datasets such that these operations can be performed.

This section is non-normative.

When data scientists discuss canonicalization, they do so in the context of achieving a particular set of goals. Since the same information may sometimes be expressed in a variety of different ways, it often becomes necessary to transform each of these different ways into a single, standard representation. With a standard representation, the differences between two different sets of data can be easily determined, a cryptographically-strong hash identifier can be generated for a particular set of data, and a particular set of data may be digitally-signed for later verification.

In particular, this specification is about normalizing RDF datasets, which are collections of graphs. Since a directed graph can express the same information in more than one way, it requires canonicalization to achieve the aforementioned goals and any others that may arise via serendipity.

Most RDF datasets can be canonicalized fairly quickly, in terms of algorithmic time complexity. However, those that contain nodes that do not have globally unique identifiers pose a greater challenge. Normalizing these datasets presents the graph isomorphism problem, a problem that is believed to be difficult to solve quickly in the worst case. Fortunately, existing real world data is rarely, if ever, modeled in a way that manifests as the worst case and new data can be modeled to avoid it. In fact, software systems that detect a problematic dataset (see 7.1 Dataset Poisoning) can choose to assume it's an attempted denial of service attack, rather than a real input, and abort.

This document outlines an algorithm for generating a canonical serialization of an RDF dataset given an RDF dataset as input. The algorithm is called the RDF Canonicalization algorithm version 1.0 or RDFC-1.0.

Note

RDF 1.1 Concepts and Abstract Syntax [RDF11-CONCEPTS] lacks clarity on the representation of language-tagged strings, where language tags of the form xx-YY are treated as being case insensitive. Implementations might represent language tags using all lower case in the form xx-yy, retain the original representation xx-YY, or use [BCP47] formatting conventions, leading to different canonical forms, and therefore, different hashed values.

The Canonicalization algorithm is based on the RDF 1.1 definition, in the sense that the language tag xx-YY is case insensitive, which might lead to different canonicalizations if the user is not aware of this problem.
User communities ought to agree to use lower case language tags, while being aware that some implementations might normalize language tags, affecting hash values.
Future evolution of RDF might regulate this issue, which RDF environments might have to adapt to, and this might lead to an update of RDFC-1.0.

Note

See B. URDNA2015 for a comparison with the version of the algorithm published in RDF Dataset Canonicalization [CCG-RDC-FINAL].

There are different use cases where graph or dataset canonicalization are important:

Determining if one serialization is isomorphic to another.
Digital signing of graphs (datasets) independent of serialization or format.
Comparing two graphs (datasets) to find differences.
Communicating change sets when remotely updating an RDF source.

A canonicalization algorithm is necessary, but not necessarily sufficient, to handle many of these use cases. The use of blank nodes in RDF graphs and datasets has a long history and creates inevitable complexities. Blank nodes are used for different purposes:

when a well known identifier for a node is not known, or the author of a document chooses not to unambiguously name that node,
when a node is used to stitch together parts of a graph and the nodes themselves are not interesting (e.g., RDF Collections in [RDF11-MT]),
when someone is trying to create an intentionally difficult graph topology.

Furthermore, RDF semantics dictate that deserializing an RDF document results in the creation of unique blank nodes, unless it can be determined that on each occasion, the blank node identifies the same resource. This is due to the fact that blank node identifiers are an aspect of a concrete RDF syntax and are not intended to be persistent or portable. Within the abstract RDF model, blank nodes do not have identifiers (although some RDF store implementations may use stable identifiers and may choose to make them portable). See Blank Nodes in [RDF11-CONCEPTS] for more information.

RDF does have a provision for allowing blank nodes to be published in an externally identifiable way through the use of Skolem IRIs, which allow a given RDF store to replace the use of blank nodes in a concrete syntax with IRIs, which then serve to repeatably identify that blank node within that particular RDF store; however, this is not generally useful for talking about the same graph in different RDF stores, or other concrete representations. In any case, a stable blank node identifier defined for one RDF store or serialization is arbitrary, and typically not relatable to the context within which it is used.

This specification defines an algorithm for creating stable blank node identifiers repeatably for different serializations possibly using individualized blank node identifiers of the same RDF graph (dataset) by grounding each blank node through the nodes to which it is connected. As a result, a graph signature can be obtained by hashing a canonical serialization of the resulting canonicalized dataset, allowing for the isomorphism and digital signing use cases. This specification does not define such a graph signature.

As blank node identifiers can be stable even with other changes to a graph (dataset), in some cases it is possible to compute the difference between two graphs (datasets), for example if changes are made only to ground triples, or if new blank nodes are introduced which do not create an automorphic confusion with other existing blank nodes. If any information which would change the generated blank node identifier, a resulting diff might indicate a greater set of changes than actually exists. Additionally, if the starting dataset is an N-Quads document, it may be possible to correlate the original blank node identifiers used within that N-Quads document with those issued in the canonicalized dataset.

Note

Although alternative hash algorithms might be used with this specification, applications ought to carefully weigh the advantages and disadvantages of using an alternative hash function. This is the case, in particular, for any representation of the canonical n-quads form or issued identifiers map that does not identify the associated hash algorithm. Any use case that requires reproduction of the same output is expected to unequivocally express or communicate the internal hash algorithm that was used when generating the canonical n-quads form.

This document is a detailed specification for an RDF dataset canonicalization algorithm. The document is primarily intended for the following audiences:

Software developers that want to implement an RDF dataset canonicalization algorithm.
Masochists.

To understand the basics in this specification you must be familiar with basic RDF concepts [RDF11-CONCEPTS]. A working knowledge of graph theory and graph isomorphism is also recommended.

This section is non-normative.

The following typographic conventions are used in this specification:

markup

Markup (elements, attributes, properties), machine processable values (string, characters, media types), property names, and file names are in red-orange monospace font.

variable

A variable in pseudo-code or in an algorithm description is italicized.

definition

A definition of a term, to be used elsewhere in this or other specifications, is italicized and in bold.

definition reference

A reference to a definition in this document is underlined and is also an active link to the definition itself.

markup definition reference

References to a definition in this document, when the reference itself is also a markup, is underlined, in a red-orange monospace font, and is also an active link to the definition itself.

external definition reference

A reference to a definition in another document is underlined and italicized, and is also an active link to the definition itself.

markup external definition reference

A reference to a definition in another document, when the reference itself is also a markup, is underlined and italicized in a red-orange monospace font, and is also an active link to the definition itself.

hyperlink

A hyperlink is underlined and in blue.

[reference]

A document reference (normative or informative) is enclosed in square brackets and links to the references section.

Explanation

An expandable area to find a more detailed, non-normative explanation of a particular algorithmic step.

Explanation

This area would provide more information about the step involved.

Logging

An expandable area to find suggestions for implementations to log information about processing, which may be useful in comparing with other implementations, or with logs provided with each test case.

Logging

For example, the following output snippet might describe the operation of an implementation using the [YAML] format.

ca:
  ca2:
    bn_to_quads:
      e0:
        - _:e0 <http://example.com/#p1> _:e1 .
      e1:
        ...
  ca3:
  - identifier: e0
    h1dq:
      nquads:
        - _:a <http://example.com/#p1> _:z .

Note

Notes are in light green boxes with a green left border and with a "Note" header in green. Notes are always informative.

Example 1

Examples are in light khaki boxes, with khaki left border,
and with a numbered "Example" header in khaki.
Examples are always informative. The content of the example is in monospace font and may be 
syntax colored.

Examples may have tabbed navigation buttons
to show the results of transforming an example into other representations.

Code examples are generally given in a Turtle or TriG format for brevity,
where each line represents a single triple or quad.
Additionally, have the following implied directives:

BASE <http://example.com/>
PREFIX : <#>

Following the Turtle/TriG syntax rules, blank nodes always appear in the 
`_:xyz` format.

Canonicalization is the process of transforming an input dataset to its serialized canonical form. That is, any two input datasets that contain the same information, regardless of their arrangement, will be transformed into the same serialized canonical form. 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.

Note

This specification defines a canonicalized dataset to include stable identifiers for blank nodes, practical uses of which will always generate a canonical serialization of such a dataset.

In time, there may be more than one canonicalization algorithm and, therefore, for identification purposes, this algorithm is named the "RDF Canonicalization algorithm version 1.0" (RDFC-1.0).

Figure 1 provides an overview of RDFC-1.0, with steps 1 through 7 corresponding to the various steps described in 4.4.3 Algorithm.

Figure 1 An illustrated overview of the RDFC-1.0 algorithm.
Image available in SVG .

This section is non-normative.

To determine a canonical labeling, RDFC-1.0 considers the information connected to each blank node. Nodes with unique first degree information can immediately be issued a canonical identifier via the Issue Identifier algorithm. When a node has non-unique first degree information, it is necessary to determine all information that is transitively connected to it throughout the entire dataset. 4.6 Hash First Degree Quads defines a node’s first degree information via its first degree hash.

Hashes are computed from the information of each blank node. These hashes encode the mentions incident to each blank node. The hash of a string s, is the lower-case, hexadecimal representation of the result of passing s through a cryptographic hash function. By default, RDFC-1.0 uses the SHA-256 hash algorithm [FIPS-180-4].

Note

The "degree" terminology is used within this specification as colloquial way of describing the eccentricity or radius of any two nodes within a dataset. This concept is also related to "degrees of separation", as in, for example, "six degrees of separation". Nodes with unique first degree information can be considered nodes with a radius of one.

When performing the steps required by the canonicalization algorithm, it is helpful to track state in a data structure called the canonicalization state. The information contained in the canonicalization state is described below.

blank node to quads map: A map that relates a blank node identifier to the quads in which they appear in the input dataset.
hash to blank nodes map: A map that relates a hash to a list of blank node identifiers.
canonical issuer: An identifier issuer, initialized with the prefix c14n (short for canonicalization), for issuing canonical blank node identifiers.
Note
Mapping all blank nodes to use this identifier spec means that an RDF dataset composed of two different RDF graphs will issue different identifiers than that for the graphs taken independently. This may happen anyway, due to automorphisms, or overlapping statements, but an identifier based on the resulting hash along with an issue sequence number specific to that hash would stand a better chance of surviving such minor changes, and allow the resulting information to be useful for RDF Diff.

The canonicalization algorithm issues identifiers to blank nodes. The Issue Identifier algorithm uses an identifier issuer to accomplish this task. The information an identifier issuer needs to keep track of is described below.

identifier prefix: The identifier prefix is a string that is used at the beginning of an blank node identifier. It should be initialized to a string that is specified by the canonicalization algorithm. When generating a new blank node identifier, the prefix is concatenated with a identifier counter. For example, c14n is a proper initial value for the identifier prefix that would produce blank node identifiers like c14n1.
identifier counter: A counter that is appended to the identifier prefix to create an blank node identifier. It is initialized to 0.
issued identifiers map: An ordered map that relates blank node identifiers to issued identifiers, to prevent issuance of more than one new identifier per existing identifier, and to allow blank nodes to be assigned identifiers some time after issuance.

The canonicalization algorithm converts an input dataset into a canonicalized dataset or raises an error if the input dataset is determined to be overly complex. This algorithm will assign deterministic identifiers to any blank nodes in the input dataset.

This section is non-normative.

RDFC-1.0 canonically labels an RDF dataset by assigning each blank node a canonical identifier. In RDFC-1.0, an RDF dataset D is represented as a set of quads of the form < s, p, o, g > where the graph component g is empty if and only if the triple < s, p, o > is in the default graph. It is expected that, for two RDF datasets, RDFC-1.0 returns the same canonically labeled list of quads if and only if the two datasets are isomorphic (i.e., the same modulo blank node identifiers).

RDFC-1.0 consists of several sub-algorithms. These sub-algorithms are introduced in the following sub-sections. First, we give a high level summary of RDFC-1.0.

Initialization. Initialize the state needed for the rest of the algorithm using 4.2 Canonicalization State. Also initialize the canonicalized dataset using the input dataset (which remains immutable) the input blank node identifier map (retaining blank node identifiers from the input if possible, otherwise assigning them arbitrarily); the issued identifiers map from the canonical issuer is added upon completion of the algorithm.
Compute first degree hashes. Compute the first degree hash for each blank node in the dataset using 4.6 Hash First Degree Quads.
Canonically label unique nodes. Assign canonical identifiers via 4.5 Issue Identifier Algorithm, in Unicode code point order, to each blank node whose first degree hash is unique.
Compute N-degree hashes for non-unique nodes. For each repeated first degree hash (proceeding in Unicode code point order), compute the N-degree hash via 4.8 Hash N-Degree Quads of every unlabeled blank node that corresponds to the given repeated hash.
Canonically label remaining nodes. In Unicode code point order of the N-degree hashes, issue canonical identifiers to each corresponding blank node using 4.5 Issue Identifier Algorithm. If more than one node produces the same N-degree hash, the order in which these nodes receive a canonical identifier does not matter.
Finish. Return the serialized canonical form of the canonicalized dataset. Alternatively, return the canonicalized dataset containing the input blank node identifier map and issued identifiers map.

This section is non-normative.

Example 2: Unique hashes

This example illustrates the Canonicalization Algorithm where all blank nodes have unique first-degree hashes.

Figure 2 An illustration of a graph resulting in unique hashes.
Image available in SVG .

:p :q _:e0 .
:p :r _:e1 .
_:e0 :s :u .
_:e1 :t :u .

Step 2 is called twice, with each blank node (e0 and e1) in the input dataset to populate blank node to quads map:

Table 1 Blank node to quads map for unique hashes
`blank node`	`Q`
`e0`	`:p :q e0 .` `e0 :s :u .`
`e1`	`:p :r e1 .` `e1 :t :u .`

Step 3 generates the first-degree hash for each blank node, which is explored further in Example 4:

Table 2 Hash to blank nodes map for unique hashes
`hash`	`blank node(s)`
`21d1dd5ba21f3dee9d76c0c00c260fa6f5d5d65315099e553026f4828d0dc77a`	`e0`
`6fa0b9bdb376852b5743ff39ca4cbf7ea14d34966b2828478fbf222e7c764473`	`e1`

Step 4 creates canonical identifiers for each blank node which has a unique hash:

Table 3 Canonical identifiers for blank nodes with unique hashes
`blank node`	`canonical identifier`
`e0`	`c14n0`
`e1`	`c14n1`

Step 5 has no effect, as there are no remaining blank nodes without canonical identifiers.

Step 6 generates the canonicalized dataset by mapping blank node identifiers in the input dataset with canonical identifiers:

:p :q _:c14n0 .
:p :r _:c14n1 .
_:c14n0 :s :u .
_:c14n1 :t :u .

Example 3: Shared hashes

This example illustrates the Canonicalization Algorithm where hashing the statements mentioning those blank nodes have overlapping results.

Figure 3 An illustration of a graph resulting in shared hashes.
Image available in SVG .

:p :q _:e0 .
:p :q _:e1 .
_:e0 :p _:e2 .
_:e1 :p _:e3 .
_:e2 :r _:e3 .

Step 2 is called four times, with each blank node (e0, e1, e2, and e3) to populate blank node to quads map:

Table 4 Blank node to quads map for shared hashes
`blank node`	`Q`
`e0`	`:p :q e0 .` `e0 :p e2 .`
`e1`	`:p :q e1 .` `e1 :p e3 .`
`e2`	`e0 :p e2 .` `e2 :r e3 .`
`e3`	`e1 :p e3 .` `e2 :r e3 .`

Step 3 generates the first-degree hash for each blank node, which is explored further in Example 5 (note that the hashes for e0 and e1 are shared):

Table 5 Hash to blank nodes map shared hashes
`hash`	`blank node(s)`
`3b26142829b8887d011d779079a243bd61ab53c3990d550320a17b59ade6ba36`	`e0`, `e1`
`15973d39de079913dac841ac4fa8c4781c0febfba5e83e5c6e250869587f8659`	`e2`
`7e790a99273eed1dc57e43205d37ce232252c85b26ca4a6ff74ff3b5aea7bccd`	`e3`

Step 4 creates canonical identifiers for each blank node which has a unique hash:

Table 6 Canonical identifiers for blank nodes with shared hashes
`blank node`	`canonical identifier`
`e2`	`c14n0`
`e3`	`c14n1`

Step 5 is run on e0 and e1, separately, which share the same hash, and use separate instances of a temporary issuer, (as explored in Example 7) to create the hash path list composed of the hash result and temporary identifier mappings from Hash N-Degree Quads algorithm for each of these blank nodes:

temporary issuer mappings

hash fbc300de5afafd97a4b9ee1e72b57754dcdcb7ebb724789ac6a94a5b82a48d30

temporary issuer mappings

hash 2c0b377baf86f6c18fed4b0df6741290066e73c932861749b172d1e5560f5045

Step 5.3 creates the canonical identifiers for the temporary identifiers established in the previous step running in order of the hash component from each result. This updates the canonical issuer with the following mappings:

Table 9 Blank node to canonical identifiers
`blank node`	`canonical identifier`
`e0`	`c14n3`
`e1`	`c14n2`
`e2`	`c14n0`
`e3`	`c14n1`

Step 6 updates the canonicalized dataset with the canonical issuer, containing an issued identifiers map mapping blank node identifiers from the input dataset to their canonical identifiers:

:p :q _:c14n2 .
:p :q _:c14n3 .
_:c14n0 :r _:c14n1 .
_:c14n2 :p _:c14n1 .
_:c14n3 :p _:c14n0 .

The following algorithm will run with a minimal number of iterations in each step for typical input datasets. In some extreme cases, the algorithm can behave poorly, particularly in Step 5. Implementations MUST defend against potential denial-of-service attacks by raising suitable exceptions and terminating early. See 7.1 Dataset Poisoning for further information.

Note

Implementations can consider placing limits on the number of calls to 4.8 Hash N-Degree Quads based on the number of blank nodes in the hash to blank nodes map. For most typical datasets, more than a couple of iterations on 4.8 Hash N-Degree Quads per blank node would be unusual.

Create the canonicalization state. If the input dataset is an N-Quads document, parse that document into a dataset in the canonicalized dataset, retaining any blank node identifiers used within that document in the input blank node identifier map; otherwise arbitrary identifiers are assigned for each blank node.

Explanation

This has the effect of initializing the blank node to quads map, and the hash to blank nodes map, as well as instantiating a new canonical issuer.

After this algorithm completes, the input blank node identifier map state and canonical issuer may be used to correlate blank nodes used in the input dataset with both their original identifiers, and associated canonical identifiers.
For every quad Q in input dataset:
1. For each blank node that is a component of Q, add a reference to Q from the map entry for the blank node identifier identifier in the blank node to quads map, creating a new entry if necessary, using the identifier for the blank node found in the input blank node identifier map.
  
  Explanation
  
  This establishes the blank node to quads map, relating each blank node with the set of quads of which it is a component, via the map for each blank node in the input dataset to its assigned identifier.
  
  Note
  Literal components of quads are not subject to any normalization. As noted in Section 3.3 of [RDF11-CONCEPTS], literal term equality is based on the lexical form, rather than the literal value, so two literals "01"^^xsd:integer and "1"^^xsd:integer are treated as distinct resources.
Logging

Log the state of the blank node to quads map:
```
# Blank node to quads map for unique hashes example
ca:
  log point: Entering the canonicalization function (4.4.3).
  ca.2:
    log point: Extract quads for each bnode (4.4.3 (2)).
    Bnode to quads:
      e0:
        - <http://example.com/#p> <http://example.com/#q> _:e0 .
        - _:e0 <http://example.com/#s> <http://example.com/#u> .
      e1:
        - <http://example.com/#p> <http://example.com/#r> _:e1 .
        - _:e1 <http://example.com/#t> <http://example.com/#u> .
  ...
```

For each key n in the blank node to quads map:

Explanation

This step creates a hash for every blank node in the input document. Some blank nodes will lead to a unique hash, while other blank nodes may share a common hash.

Create a hash, h_f(n), for n according to the Hash First Degree Quads algorithm.
Append n to the value associated to h_f(n) in hash to blank nodes map, creating a new entry if necessary.

Logging

Log the results from the Hash First Degree Quads algorithm.

# First degree hashes for unique hashes example
ca:
  ...
  ca.3:
    log point: Calculated first degree hashes (4.4.3 (3)).
    with:
      - identifier: e0
        h1dq:
          log point: Hash First Degree Quads function (4.6.3).
          nquads:
            - <http://example.com/#p> <http://example.com/#q> _:a .
            - _:a <http://example.com/#s> <http://example.com/#u> .
          hash: 21d1dd5ba21f3dee9d76c0c00c260fa6f5d5d65315099e553026f4828d0dc77a
      - identifier: e1
        h1dq:
          log point: Hash First Degree Quads function (4.6.3).
          nquads:
            - <http://example.com/#p> <http://example.com/#r> _:a .
            - _:a <http://example.com/#t> <http://example.com/#u> .
          hash: 6fa0b9bdb376852b5743ff39ca4cbf7ea14d34966b2828478fbf222e7c764473
  ...

For each hash to identifier list map entry in hash to blank nodes map, code point ordered by hash:

Explanation

This step establishes the canonical identifier for blank nodes having a unique hash, which are recorded in the canonical issuer.
1. If identifier list has more than one entry, continue to the next mapping.
2. Use the Issue Identifier algorithm, passing canonical issuer and the single blank node identifier, identifier in identifier list to issue a canonical replacement identifier for identifier.
3. Remove the map entry for hash from the hash to blank nodes map.
Logging

Log the assigned canonical identifiers.
```
# Assigned canonical identifiers for shared hashes example
ca:
  ...
  ca.4:
    log point: Create canonical replacements for hashes mapping to a single node (4.4.3 (4)).
    with:
      - identifier: e2
        hash: 15973d39de079913dac841ac4fa8c4781c0febfba5e83e5c6e250869587f8659
        canonical label: c14n0
      - identifier: e3
        hash: 7e790a99273eed1dc57e43205d37ce232252c85b26ca4a6ff74ff3b5aea7bccd
        canonical label: c14n1
  ...
```
For each hash to identifier list map entry in hash to blank nodes map, code point ordered by hash:

Explanation

This step establishes the canonical identifier for blank nodes having a shared hash. This is done by creating unique blank node identifiers for all blank nodes traversed by the Hash N-Degree Quads algorithm, running through each blank node without a canonical identifier in the order of the hashes established in the previous step.
Logging

Log hash and identifier list for this iteration.
```
# Hash and Identifier List for each iteration of step 5 using shared hashes example
ca:
  ...
  ca.5:
    log point: Calculate hashes for identifiers with shared hashes (4.4.3 (5)).
    with:
      - hash: 3b26142829b8887d011d779079a243bd61ab53c3990d550320a17b59ade6ba36
        identifier list: [ "e0", "e1"]
    ...
  ...
```
1. Create hash path list where each item will be a result of running the Hash N-Degree Quads algorithm.
  
  Explanation
  
  This list will be populated in step 5.2, and will establish an order for those blank nodes sharing a common first-degree hash.
2. For each blank node identifier n in identifier list:
  1. If a canonical identifier has already been issued for n, continue to the next blank node identifier.
  2. Create temporary issuer, an identifier issuer initialized with the prefix b.
  3. Use the Issue Identifier algorithm, passing temporary issuer and n, to issue a new temporary blank node identifier b_n to n.
  4. Run the Hash N-Degree Quads algorithm, passing the canonicalization state, n for identifier, and temporary issuer, appending the result to the hash path list.
    Logging
    
    Include logs for each call to Hash N-Degree Quads algorithm.
```
# Logs from calls to Hash N-Degree Quads algorithm for shared hashes example
ca:
  ...
  ca.5:
    log point: Calculate hashes for identifiers with shared hashes (4.4.3 (5)).
    with:
      - hash: 3b26142829b8887d011d779079a243bd61ab53c3990d550320a17b59ade6ba36
        identifier list: [ "e0", "e1"]
        ca.5.2:
          log point: Calculate hashes for identifiers with shared hashes (4.4.3 (5.2)).
          with:
            - identifier: e0
              hndq:
                log point: Hash N-Degree Quads function (4.8.3).
                identifier: e0
                issuer: {e0: b0}
                ...
            ...
        ...
  ...
```
3. For each result in the hash path list, code point ordered by the hash in result:
  
  Explanation
  
  The previous step created temporary identifiers for the blank nodes sharing a common first degree hash, which is now used to generate their canonical identifiers.
  1. For each blank node identifier, existing identifier, that was issued a temporary identifier by identifier issuer in result, issue a canonical identifier, in the same order, using the Issue Identifier algorithm, passing canonical issuer and existing identifier.
    
    Explanation
    
    In Step 5.2, hash path list was created with an ordered set of results. Each result contained a temporary issuer which recorded temporary identifiers associated with a particular blank node identifier in identifier list. This step processes each returned temporary issuer, in order, and allocates canonical identifiers to the temporary identifier mappings contained within each temporary issuer, creating a full order on the remaining blank nodes with unissued canonical identifiers.
  Logging
  
  Log newly issued canonical identifiers.
```
# Newly issued canonical identifiers from step 5.3 for shared hashes example
ca:
  ...
  ca.5:
    log point: Calculate hashes for identifiers with shared hashes (4.4.3 (5)).
    with:
      - hash: 3b26142829b8887d011d779079a243bd61ab53c3990d550320a17b59ade6ba36
        identifier list: [ "e0", "e1"]
        ...
        ca.5.3:
          log point: Canonical identifiers for temporary identifiers (4.4.3 (5.3)).
          issuer:
              - blank node: e1
                canonical identifier: c14n2
              - blank node: e0
                canonical identifier: c14n3
  ...
```
Add the issued identifiers map from the canonical issuer to the canonicalized dataset.

Explanation

This step adds the issued identifiers map from the canonical issuer to the canonicalized dataset, the keys in the issued identifiers map are map entries in the input blank node identifier map.
Logging

Log the state of the canonical issuer at the completion of the algorithm.
```
# Canonical issuer state after step 6 for shared hashes example
ca:
  ...
  ca.6:
    log point: Issued identifiers map (4.4.3 (6)).
    issued identifiers map: {e2: c14n0, e3: c14n1, e1: c14n2, e0: c14n3}
```
Return the serialized canonical form of the canonicalized dataset. Upon request, alternatively (or additionally) return the canonicalized dataset itself, which includes the input blank node identifier map, and issued identifiers map from the canonical issuer.
Note
Technically speaking, one implementation might return a canonicalized dataset that maps particular blank nodes to different identifiers than another implementation, however, this only occurs when there are isomorphisms in the dataset such that a canonically serialized expression of the dataset would appear the same from either implementation.

Explanation

The serialized canonical form is an N-Quads document where the blank node identifiers are taken from the canonical identifiers associated with each blank node.

The canonicalized dataset is composed of the original input dataset, the input blank node identifier map, containing identifiers for each blank node in the input dataset, and the canonical issuer, containing an issued identifiers map mapping the identifiers in the input blank node identifier map to their canonical identifiers.

This algorithm issues a new blank node identifier for a given existing blank node identifier. It also updates state information that tracks the order in which new blank node identifiers were issued. The order of issuance is important for canonically labeling blank nodes that are isomorphic to others in the dataset.

The algorithm maintains an issued identifiers map to relate an existing blank node identifier from the input dataset to a new blank node identifier using a given identifier prefix (c14n) with new identifiers issued by appending an incrementing number. For example, when called for a blank node identifier such as e3, it might result in a issued identifier of c14n1.

The algorithm takes an identifier issuer I and an existing identifier as inputs. The output is a new issued identifier. The steps of the algorithm are:

If there is a map entry for existing identifier in issued identifiers map of I, return it.
Generate issued identifier by concatenating identifier prefix with the string value of identifier counter.
Add an entry mapping existing identifier to issued identifier to the issued identifiers map of I.
Increment identifier counter.
Return issued identifier.

This algorithm calculates a hash for a given blank node across the quads in a dataset in which that blank node is a component. If the hash uniquely identifies that blank node, no further examination is necessary. Otherwise, a hash will be created for the blank node using the algorithm in 4.8 Hash N-Degree Quads invoked via 4.4 Canonicalization Algorithm.

This section is non-normative.

To determine whether the first degree information of a node n is unique, a hash is assigned to its mention set, Q_n. The first degree hash of a blank node n, denoted h_f(n), is the hash that results from 4.6 Hash First Degree Quads when passing n. Nodes with unique first degree hashes have unique first degree information.

For consistency, blank node identifiers used in Q_n are replaced with placeholders in a canonical n-quads serialization of that quad. Every blank node component is replaced with either a or z, depending on if that component is n or not.

The resulting serialized quads are then code point ordered, concatenated, and hashed. This hash is the first degree hash of n, h_f(n).

This section is non-normative.

Example 4: Unique hashes

This example illustrates hashing quads containing blank nodes where hashing the statements mentioning those blank nodes generates unique results.

Figure 4 An illustration of a graph resulting in unique hashes.
Image available in SVG .

:p :q _:e0 .
:p :r _:e1 .
_:e0 :s :u .
_:e1 :t :u .

The algorithm will be called twice, with each blank node (e0 and e1). Running the algorithm with the reference node e0 results in the following quads, after replacing blank nodes:

:p :q _:a .
_:a :s :u .

These are then serialized to canonical n-quads form: '<http://example.com/#p> <http://example.com/#q> _:a .\n_:a <http://example.com/#s> <http://example.com/#u> .\n', concatenated and hashed using the hash algorithm (SHA-256) resulting in 21d1dd5ba21f3dee9d76c0c00c260fa6f5d5d65315099e553026f4828d0dc77a.

The algorithm is run a second time with the reference node e1 resulting in the following quads:

:p :r _:a .
_:a :t :u .

These are then serialized to canonical n-quads form: '<http://example.com/#p> <http://example.com/#r> _:a .\n_:a <http://example.com/#t> <http://example.com/#u> .\n', concatenated and hashed as before resulting in 6fa0b9bdb376852b5743ff39ca4cbf7ea14d34966b2828478fbf222e7c764473.

Thus the generated hashes each reference just a single blank node, allowing the canonicalization algorithm to use only the Hash First Degree Quads algorithm.

Example 5: Shared hashes

This example illustrates hashing quads containing blank nodes where hashing the statements mentioning those blank nodes have overlapping results.

Figure 5 An illustration of a graph resulting in shared hashes.
Image available in SVG .

:p :q _:e0 .
:p :q _:e1 .
_:e0 :p _:e2 .
_:e1 :p _:e3 .
_:e2 :r _:e3 .

The algorithm will be called four times, with each blank node (e0, e1, e2, and e3). Running the algorithm with the reference node e0 results in the following quads, after replacing blank nodes:

:p :q _:a .
_:a :p _:z .

Which hashes to: 3b26142829b8887d011d779079a243bd61ab53c3990d550320a17b59ade6ba36.

Note that using reference node e1 results in the same quads, and thus results in the same hash.

Using the reference node e2 results in the following quads:

_:z :p _:a .
_:a :r _:z .

Which hashes to: 15973d39de079913dac841ac4fa8c4781c0febfba5e83e5c6e250869587f8659.

Lastly, using the reference node e3 results in the following quads:

_:z :p _:a .
_:z :r _:a .

Which hashes to: 7e790a99273eed1dc57e43205d37ce232252c85b26ca4a6ff74ff3b5aea7bccd.

The hashes for e2 and e3 are unique, but e0, and e1 share a common hash, which will require the use of the Hash N-Degree Quads Algorithm, as it is necessary to consider quads further removed from the direct mentions to determine a unique hash.

This algorithm takes the canonicalization state and a reference blank node identifier as inputs.

Initialize nquads to an empty list. It will be used to store quads in canonical n-quads form.
Get the list of quads quads from the map entry for reference blank node identifier in the blank node to quads map.
For each quad quad in quads:
1. Serialize the quad in canonical n-quads form with the following special rule:
  1. If any component in quad is an blank node, then serialize it using a special identifier as follows:
    1. If the blank node's existing blank node identifier matches the reference blank node identifier then use the blank node identifier a, otherwise, use the blank node identifier z.
Sort nquads in Unicode code point order.

Return the hash that results from passing the sorted and concatenated nquads through the hash algorithm.

Logging

Log the inputs and result of running this algorithm.

# Inputs and hash result for the Hash First Degree Hash algorithm for unique hashes example
h1dq:
  log point: Hash First Degree Quads function (4.6.3).
  nquads:
    - <http://example.com/#p> <http://example.com/#q> _:a .
    - _:a <http://example.com/#s> <http://example.com/#u> .
  hash: 21d1dd5ba21f3dee9d76c0c00c260fa6f5d5d65315099e553026f4828d0dc77a

This algorithm generates a hash for some blank node component of a quad, considering its position within that quad. This is used as part of the Hash N-Degree Quads algorithm to characterize the blank nodes related to some particular blank node within their mention sets.

This algorithm creates a hash to identify how one blank node is related to another. It takes the canonicalization state, a related blank node identifier, a quad, an identifier issuer, issuer, and a string position as inputs.

Initialize a string input to the value of position.
If position is not g, append <, the value of the predicate in quad, and > to input.
If there is a canonical identifier for related, or an identifier issued by issuer, append the string _:, followed by that identifier (using the canonical identifier if present, otherwise the one issued by issuer) to input.

Explanation

If a canonical identifier was already issued for related, it will be in the canonical issuer contained within canonicalization state. Otherwise, the temporary issuer instance may already have a mapping for related.
Otherwise, append the result of the Hash First Degree Quads algorithm, passing related to input.

Explanation

If no identifier, canonical or temporary, has already been issued, a new identifier is created using the Hash First Degree Quads algorithm.

Note that Hash First Degree Quads algorithm has already been called on all blank nodes in step 3 of the Canonicalization algorithm. Implementations should consider reusing a previously determined result rather than execute the Hash First Degree Quads algorithm again.
Return the hash that results from passing input through the hash algorithm.

Explanation

This resulting string is used to generate a hash; in this respect, it is similar to the Hash First Degree Quads algorithm which uses the serialization of quads in nquads for hashing. For the sake of consistency, the nquad representation of identifier is used in this step, hence the appearance of the _: string.
Logging

Log the inputs and result of running this algorithm.
```
# Inputs and hash result for the Hash Related Blank algorithm for shared hashes example
hndq3.1:
  log point: Hash related bnode component (4.8.3 (3.1))
  with:
    - position: o
      related: e2
      input: "o<http://example.com/#p>_:c14n0"
      hash: 29cf7e22790bc2ed395b81b3933e5329fc7b25390486085cac31ce7252ca60fa
```

This algorithm calculates a hash for a given blank node across the quads in a dataset in which that blank node is a component for which the hash does not uniquely identify that blank node. This is done by expanding the search from quads directly referencing that blank node (the mention set), to those quads which contain nodes which are also components of quads in the mention set, called the gossip path. This process proceeds in every greater degrees of indirection until a unique hash is obtained.

This section is non-normative.

Usually, when trying to determine if two nodes in a graph are equivalent, you simply compare their identifiers. However, what if the nodes don't have identifiers? Then you must determine if the two nodes have equivalent connections to equivalent nodes all throughout the whole graph. This is called the graph isomorphism problem. This algorithm approaches this problem by considering how one might draw a graph on paper. You can test to see if two nodes are equivalent by drawing the graph twice. The first time you draw the graph the first node is drawn in the center of the page. If you can draw the graph a second time such that it looks just like the first, except the second node is in the center of the page, then the nodes are equivalent. This algorithm essentially defines a deterministic way to draw a graph where, if you begin with a particular node, the graph will always be drawn the same way. If two graphs are drawn the same way with two different nodes, then the nodes are equivalent. A hash is used to indicate a particular way that the graph has been drawn and can be used to compare nodes.

When two blank nodes have the same first degree hash, extra steps must be taken to detect global, or N-degree, distinctions. All information that is in any way connected to the blank node n through other blank nodes, even transitively, must be considered.

To consider all transitive information, the algorithm traverses and encodes all possible paths of incident mentions emanating from n, called gossip paths, that reach every unlabeled blank node connected to n. Each unlabeled blank node is assigned a temporary identifier in the order in which it is reached in the gossip path being explored. The mentions that are traversed to reach connected blank nodes are encoded in these paths via related hashes. This provides a deterministic way to order all paths coming from n that reach all blank nodes connected to n without relying on input blank node identifiers.

This algorithm works in concert with the main canonicalization algorithm to produce a unique, deterministic identifier for a particular blank node. This hash incorporates all of the information that is connected to the blank node as well as how it is connected. It does this by creating deterministic paths that emanate out from the blank node through any other adjacent blank nodes.

Ultimately, the algorithm selects the shortest gossip path (based on its encoding as a string), distributing canonical identifiers to the unlabeled blank nodes in the order in which they appear in this path. The hash of this encoded shortest path, called the N-degree hash of n, distinguishes n from other blank nodes in the dataset.

For clarity, we consider a gossip path encoded via the string s to be shortest provided that:

The length of s is less than or equal to the length of any other gossip path string s′.
If s and s′ have the same length (as strings), then s is code point ordered less than or equal to s′.

For example, abc is shorter than bbc, whereas abcd is longer than bcd.

The following provides a high level outline for how the N-degree hash of n is computed along the shortest gossip path. Note that the full algorithm considers all gossip paths, ultimately returning the hash of the shortest encoded path.

Compute related hashes. Compute the related hash H_n set for n, i.e., all first degree mentions between n and another blank node. Note that this includes both unlabeled blank nodes and those already issued a canonical identifier (labeled blank nodes).
Explore mentions. Given the related hash x in H_n, record x in the data to hash D_n. Determine whether each blank node reachable via the mention with related hash x has already received an identifier.
1. Record the identifiers of labeled nodes. If a blank node already has an identifier, record its identifier in D_n once for every mention with related hash x. Skip to the next related hash in H_n and repeat step 2.
2. Distribute and record temporary identifiers to unlabeled nodes. For each unlabeled blank node, assign it a temporary identif[Truncated]

RDF Dataset Canonicalization

A Standard RDF Dataset Canonicalization Algorithm

Abstract

Status of This Document

1. Introduction

1.1 Uses of Dataset Canonicalization

1.2 How to Read this Document

1.3 Typographical conventions

2. Conformance

3. Terminology

3.1 Terms defined by this specification

3.2 Terms defined by cited specifications

4. Canonicalization

4.1 Overview

4.2 Canonicalization State

4.3 Blank Node Identifier Issuer State

4.4 Canonicalization Algorithm

4.4.1 Overview

4.4.2 Examples

4.4.3 Algorithm

4.5 Issue Identifier Algorithm

4.5.1 Overview

4.5.2 Algorithm

4.6 Hash First Degree Quads

4.6.1 Overview

4.6.2 Examples

4.6.3 Algorithm

4.8 Hash N-Degree Quads

4.8.1 Overview

RDF Dataset Canonicalization

A Standard RDF Dataset Canonicalization Algorithm

Abstract

Status of This Document

1. Introduction

1.1 Uses of Dataset Canonicalization

1.2 How to Read this Document

1.3 Typographical conventions

2. Conformance

3. Terminology

3.1 Terms defined by this specification

3.2 Terms defined by cited specifications

4. Canonicalization

4.1 Overview

4.2 Canonicalization State

4.3 Blank Node Identifier Issuer State

4.4 Canonicalization Algorithm

4.4.1 Overview

4.4.2 Examples

4.4.3 Algorithm

4.5 Issue Identifier Algorithm

4.5.1 Overview

4.5.2 Algorithm

4.6 Hash First Degree Quads

4.6.1 Overview

4.6.2 Examples

4.6.3 Algorithm

4.7 Hash Related Blank Node

4.7.1 Overview

4.7.2 Examples

4.7.3 Algorithm

4.8 Hash N-Degree Quads

4.8.1 Overview