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JSON [RFC4627] has proven to be a highly useful object serialization and messaging format. In an attempt to harmonize the representation of Linked Data in JSON, this specification outlines a common JSON representation format for expressing directed graphs; mixing both Linked Data and non-Linked Data in a single document.
This document is merely a public working draft of a potential specification. It has no official standing of any kind and does not represent the support or consensus of any standards organisation.
This document is an experimental work in progress.
JSON, as specified in [RFC4627], is a simple language for representing data on the Web. Linked Data is a technique for describing content across different documents or Web sites. Web resources are described using IRIs, and typically are dereferencable entities that may be used to find more information, creating a "Web of Knowledge". JSON-LD is intended to be a simple publishing method for expressing not only Linked Data in JSON, but for adding semantics to existing JSON.
JSON-LD is designed as a light-weight syntax that can be used to express Linked Data. It is primarily intended to be a way to express Linked Data in Javascript and other Web-based programming environments. It is also useful when building interoperable Web Services and when storing Linked Data in JSON-based document storage engines. It is practical and designed to be as simple as possible, utilizing the large number of JSON parsers and existing code that is in use today. It is designed to be able to express key-value pairs, RDF data, RDFa [RDFA-CORE] data, Microformats [MICROFORMATS] data, and Microdata [MICRODATA]. That is, it supports every major Web-based structured data model in use today.
The syntax does not require many applications to change their JSON, but easily add meaning by adding context in a way that is either in-band or out-of-band. The syntax is designed to not disturb already deployed systems running on JSON, but provide a smooth migration path from JSON to JSON with added semantics. Finally, the format is intended to be fast to parse, fast to generate, stream-based and document-based processing compatible, and require a very small memory footprint in order to operate.
This document is a detailed specification for a serialization of JSON for Linked data. The document is primarily intended for the following audiences:
To understand the basics in this specification you must first be familiar with JSON, which is detailed in [RFC4627]. To understand the API and how it is intended to operate in a programming environment, it is useful to have working knowledge of the JavaScript programming language [ECMA-262] and WebIDL [WEBIDL]. To understand how JSON-LD maps to RDF, it is helpful to be familiar with the basic RDF concepts [RDF-CONCEPTS].
Examples may contain references to existing vocabularies and use abbreviations in CURIEs and source code. The following is a list of all vocabularies and their abbreviations, as used in this document:
dc
, e.g., dc:title
)foaf
, e.g., foaf:knows
)rdf
, e.g., rdf:type
)xsd
, e.g., xsd:integer
)JSON [RFC4627] defines several terms which are used throughout this document:
There are a number of ways that one may participate in the development of this specification:
The following section outlines the design goals and rationale behind the JSON-LD markup language.
A number of design considerations were explored during the creation of this markup language:
The following definition for Linked Data is the one that will be used for this specification.
Note that the definition for Linked Data above is silent on the topic of unlabeled nodes. Unlabeled nodes are not considered Linked Data. However, this specification allows for the expression of unlabled nodes, as most graph-based data sets on the Web contain a number of associated nodes that are not named and thus are not directly de-referenceable.
An Internationalized Resource Identifier (IRI), as described in [RFC3987], is a mechanism for representing unique identifiers on the web. In Linked Data, an IRI is commonly used for expressing a subject, a property or an object.
JSON-LD defines a mechanism to map JSON values to IRIs. This does not mean that JSON-LD requires every key or value to be an IRI, but rather ensures that keys and values can be mapped to IRIs if the developer so desires to transform their data into Linked Data. There are a few techniques that can ensure that developers will generate good Linked Data for the Web. JSON-LD formalizes those techniques.
We will be using the following JSON markup as the example for the rest of this section:
{ "name": "Manu Sporny", "homepage": "http://manu.sporny.org/", "avatar": "http://twitter.com/account/profile_image/manusporny" }
In JSON-LD, a context is used to allow developers to map terms
to IRIs. A term is a short word that may be expanded
to an IRI. The semantic web, just like the document-based
web, uses IRIs for unambiguous identification. The idea is that these
terms mean something that may be of use to other developers.
For example, the term name
may map directly to the IRI
http://xmlns.com/foaf/0.1/name
. This allows JSON-LD documents to
be constructed using the common JSON practice of simple name/value pairs while
ensuring that the data is useful outside of the database or page in which it
resides.
These Linked Data terms are typically collected in a context and then used by adding a single line to the JSON markup above:
{
"@context": "http://example.org/json-ld-contexts/person",
"name": "Manu Sporny",
"homepage": "http://manu.sporny.org/",
"avatar": "http://twitter.com/account/profile_image/manusporny"
}
The addition above transforms the previous JSON document into a JSON document
with added semantics because the @context
specifies how the
name, homepage, and avatar
terms map to IRIs.
Mapping those keys to IRIs gives the data global context. If two
developers use the same IRI to describe a property, they are more than likely
expressing the same concept. This allows both developers to re-use each others
data without having to agree to how their data will inter-operate on a
site-by-site basis.
The semantic web uses a special type of document called a Web Vocabulary to define terms. A context is a type of Web vocabulary. Typically, these Web Vocabulary documents have prefixes associated with them and contain a number of term declarations. A prefix, like a term, is a short word that expands to a Web Vocabulary IRI. Prefixes are helpful when a developer wants to mix multiple vocabularies together in a context, but does not want to go to the trouble of defining every single term in every single vocabulary. Some Web Vocabularies may have 10-20 terms defined. If a developer wants to use 3-4 different vocabularies, the number of terms that would have to be declared in a single context would become quite large. To reduce the number of different terms that must be defined, JSON-LD also allows prefixes to be used to compact IRIs.
For example, the IRI http://xmlns.com/foaf/0.1/
specifies a Web Vocabulary which may be represented using the
foaf
prefix. The foaf
Web Vocabulary
contains a term called name. If you join the
foaf
prefix with the name suffix,
you can build a compact IRI that will expand out into an absolute IRI for the
http://xmlns.com/foaf/0.1/name
vocabulary term.
That is, the compact IRI, or short-form, is foaf:name
and the
expanded-form is http://xmlns.com/foaf/0.1/name
. This vocabulary
term is used to specify a person's name.
Developers, and machines, are able to use this IRI (plugging it directly into a web browser, for instance) to go to the term and get a definition of what the term means. Much like we can use WordNet today to see the definition of words in the English language. Developers and machines need the same sort of dictionary of terms. IRIs provide a way to ensure that these terms are unambiguous.
The context provides a collection of vocabulary terms and prefixes that can be used to expand JSON keys and values into IRIs.
In the previous section, the developer used the @context
keyword to pull in an external context. That context document, if
de-referenced, would look something like this:
{ "name": "http://xmlns.com/foaf/0.1/name", "homepage": "http://xmlns.com/foaf/0.1/homepage", "avatar": "http://xmlns.com/foaf/0.1/avatar" }
A JSON-LD context document is a simple mapping from terms and prefixes to expanded values such as IRIs or keywords. Contexts may also contain datatype information for certain terms as well as other processing instructions for the JSON-LD processor.
Contexts may be specified in-line. This ensures that JSON-LD documents can be processed when a JSON-LD processor does not have access to the Web.
JSON-LD strives to ensure that developers don't have to change the JSON that is going into and being returned from their Web applications. This means that developers can also specify a context for JSON data in an out-of-band fashion via the API. The API is described later in this document. A JSON-LD aware Web Service may also define a context that will be pre-loaded for all calls to the service. This allows services that have previously been publishing and receiving JSON data to accept JSON-LD data without requiring client software to change.
If a set of terms such as, name, homepage, and avatar, are defined in a context, and that context is used to resolve the names in JSON objects, machines are able to automatically expand the terms to something meaningful and unambiguous, like this:
{ "http://xmlns.com/foaf/0.1/name": "Manu Sporny", "http://xmlns.com/foaf/0.1/homepage": "http://manu.sporny.org" "http://rdfs.org/sioc/ns#avatar": "http://twitter.com/account/profile_image/manusporny" }
Doing this allows JSON to be unambiguously machine-readable without requiring developers that use JSON to drastically change their workflow.
JSON-LD is designed to ensure that Linked Data concepts can be marked up in a way that is simple to understand and author by Web developers. In many cases, regular JSON markup can become Linked Data with the simple addition of a context. As more JSON-LD features are used, more semantics are added to the JSON markup.
Expressing IRIs are fundamental to Linked Data as that is how most subjects and many object are named. IRIs can be expressed in a variety of different ways in JSON-LD.
@context
and when dealing with keys that
start with the @subject
character.@subject
,
if it is a string.@type
.@iri
keyword.@coerce
rules in
effect for a key named @iri
.IRIs can be expressed directly in the key position like so:
{
...
"http://xmlns.com/foaf/0.1/name": "Manu Sporny",
...
}
In the example above, the key
http://xmlns.com/foaf/0.1/name
is interpreted as an IRI, as
opposed to being interpreted as a string.
Term expansion occurs for IRIs if a term is defined within the active context:
{ "@context": {"name": "http://xmlns.com/foaf/0.1/name"}, ... "name": "Manu Sporny", ... }
Prefixes are expanded when used in keys:
{ "@context": {"name": "http://xmlns.com/foaf/0.1/name"}, ... "name": "Manu Sporny", ... }
name
above will automatically expand out to the IRI
http://xmlns.com/foaf/0.1/name
.
An IRI is generated when a value is associated with a key using
the @iri
keyword:
{
...
"homepage": { "@iri": "http://manu.sporny.org" }
...
}
If type coercion rules are specified in the @context
for
a particular vocabulary term, an IRI is generated:
{
"@context":
{
...
"@coerce":
{
"@iri": "homepage"
}
}
...
"homepage": "http://manu.sporny.org/",
...
}
Even though the value http://manu.sporny.org/
is a string,
the type coercion rules will transform the value into an IRI when processed
by a JSON-LD Processor
IRIs are a fundamental concept of Linked Data, and nodes should have a de-referencable identifier used to name and locate them. For nodes to be truely linked, de-referencing the identifier should result in a representation of that node. Associating an IRI with a node tells an application that the returned document contains a description of of the identifier requested.
JSON-LD documents may also contain descriptions of other nodes, so it is necessary to be able to uniquely identify each node which may be externally referenced.
A subject of a node is declared using the @subject
key. The subject is the
first piece of information needed by the JSON-LD processor in order to
create the (subject, property, object) tuple, also known as a triple.
{ ... "@subject": "http://example.org/people#joebob", ... }
The example above would set the subject to the IRI
http://example.org/people#joebob
.
The type of a particular subject can be specified using the
@type
key. Specifying the type in this way will generate a
triple of the form (subject, type, type-uri).
To be Linked Data, types should be uniquely identified by an IRI.
{ ... "@subject": "http://example.org/people#joebob", "@type": "http://xmlns.com/foaf/0.1/Person", ... }
The example above would generate the following triple if the JSON-LD document is mapped to RDF (in N-Triples notation):
<http://example.org/people#joebob> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> .
Regular text strings, also referred to as plain literals, are easily expressed using regular JSON strings.
{
...
"name": "Mark Birbeck",
...
}
JSON-LD makes an assumption that strings with associated language encoding information are not very common when used in JavaScript and Web Services. Thus, it takes a little more effort to express strings with associated language information.
{
...
"name":
{
"@literal": "花澄",
"@language": "ja"
}
...
}
The example above would generate a plain literal for
花澄 and associate the ja
language code with the triple
that is generated. Languages must be expressed in [BCP47] format.
A value with an associated datatype, also known as a typed literal, is indicated by associating a literal with an IRI which indicates the typed literal's datatype. Typed literals may be expressed in JSON-LD in three ways:
@coerce
keyword.The first example uses the @coerce
keyword to express a
typed literal:
{
"@context":
{
"modified": "http://purl.org/dc/terms/modified",
"dateTime": "http://www.w3.org/2001/XMLSchema#dateTime"
"@coerce":
{
"dateTime": "modified"
}
}
...
"modified": "2010-05-29T14:17:39+02:00",
...
}
The second example uses the expanded form for specifying objects:
{
...
"modified":
{
"@literal": "2010-05-29T14:17:39+02:00",
"@datatype": "dateTime"
}
...
}
Both examples above would generate an object with the literal value of
2010-05-29T14:17:39+02:00
and the datatype of
http://www.w3.org/2001/XMLSchema#dateTime
.
The third example uses a built-in native JSON type, a number, to express a datatype:
{
...
"@subject": "http://example.org/people#joebob",
"age": 31
...
}
The example above would generate the following triple:
<http://example.org/people#joebob> <http://xmlns.com/foaf/0.1/age> "31"^^<http://www.w3.org/2001/XMLSchema#integer> .
A JSON-LD author can express multiple triples in a compact way by using arrays. If a subject has multiple values for the same property, the author may express each property as an array.
In JSON-LD, Multiple objects on a property are not ordered. This is because typically graphs are not inherently ordered data structures. To see more on creating ordered collections in JSON-LD, see Lists.
{
...
"@subject": "http://example.org/people#joebob",
"nick": ["joe", "bob", "jaybee"],
...
}
The markup shown above would generate the following triples:
<http://example.org/people#joebob> <http://xmlns.com/foaf/0.1/nick> "joe" . <http://example.org/people#joebob> <http://xmlns.com/foaf/0.1/nick> "bob" . <http://example.org/people#joebob> <http://xmlns.com/foaf/0.1/nick> "jaybee" .
Multiple typed literals may also be expressed using the expanded form for objects:
{
...
"@subject": "http://example.org/articles/8",
"modified":
[
{
"@literal": "2010-05-29T14:17:39+02:00",
"@datatype": "dateTime"
},
{
"@literal": "2010-05-30T09:21:28-04:00",
"@datatype": "dateTime"
}
]
...
}
The markup shown above would generate the following triples:
<http://example.org/articles/8> <http://purl.org/dc/terms/modified> "2010-05-29T14:17:39+02:00"^^http://www.w3.org/2001/XMLSchema#dateTime . <http://example.org/articles/8> <http://purl.org/dc/terms/modified> "2010-05-30T09:21:28-04:00"^^http://www.w3.org/2001/XMLSchema#dateTime .
Expansion is the process of taking a JSON-LD document and applying a context such that all IRI, datatypes, and literal values are expanded so that the context is no longer necessary. JSON-LD document expansion is typically used when re-mapping JSON-LD documents to application-specific JSON documents or as a part of the Normalization process.
For example, assume the following JSON-LD input document:
{ "name": "Manu Sporny", "homepage": "http://manu.sporny.org/", "@context": { "name": "http://xmlns.com/foaf/0.1/name", "homepage": "http://xmlns.com/foaf/0.1/homepage", "@coerce": { "@iri": "homepage" } } }
Running the JSON-LD Expansion algorithm against the JSON-LD input document provided above would result in the following output:
{ "http://xmlns.com/foaf/0.1/name": "Manu Sporny", "http://xmlns.com/foaf/0.1/homepage": { "@iri": "http://manu.sporny.org/" } }
Compaction is the process of taking a JSON-LD document and applying a context such that the most compact form of the document is generated. JSON is typically expressed in a very compact, key-value format. That is, full IRIs are rarely used as keys. At times, a JSON-LD document may be received that is not in its most compact form. JSON-LD, via the API, provides a way to compact a JSON-LD document.
For example, assume the following JSON-LD input document:
{ "http://xmlns.com/foaf/0.1/name": "Manu Sporny", "http://xmlns.com/foaf/0.1/homepage": { "@iri": "http://manu.sporny.org/" } }
Additionally, assume the following developer-supplied JSON-LD context:
{ "name": "http://xmlns.com/foaf/0.1/name", "homepage": "http://xmlns.com/foaf/0.1/homepage", "@coerce": { "@iri": ["homepage"] } }
Running the JSON-LD Compaction algorithm given the context supplied above against the JSON-LD input document provided above would result in the following output:
{ "name": "Manu Sporny", "homepage": "http://manu.sporny.org/", "@context": { "name": "http://xmlns.com/foaf/0.1/name", "homepage": "http://xmlns.com/foaf/0.1/homepage", "@coerce": { "@iri": "homepage" } } }
The compaction algorithm also enables the developer to map any expanded
format into an application-specific compacted format. While the context
provided above mapped http://xmlns.com/foaf/0.1/name
to
name, it could have also mapped it to any arbitrary string
provided by the developer.
A JSON-LD document is a representation of a directed graph. A single directed graph can have many different serializations, each expressing exactly the same information. Developers typically work with trees, represented as JSON objects. While mapping a graph to a tree can be done, the layout of the end result must be specified in advance. A Frame can be used by a developer on a JSON-LD document to specify a deterministic layout for a graph.
Framing is the process of taking a JSON-LD document, which expresses a graph of information, and applying a specific graph layout (called a Frame).
The JSON-LD document below expresses a library, a book and a chapter:
{ "@coerce": { "Book": "http://example.org/vocab#Book", "Chapter": "http://example.org/vocab#Chapter", "contains": "http://example.org/vocab#contains", "creator": "http://purl.org/dc/terms/creator" "description": "http://purl.org/dc/terms/description" "Library": "http://example.org/vocab#Library", "title": "http://purl.org/dc/terms/title", "@coerce": { "@iri": "ex:contains" }, }, "@subject": [{ "@subject": "http://example.com/library", "@type": "Library", "contains": "http://example.org/library/the-republic" }, { "@subject": "http://example.org/library/the-republic", "@type": "Book", "creator": "Plato", "title": "The Republic", "contains": "http://example.org/library/the-republic#introduction" }, { "@subject": "http://example.org/library/the-republic#introduction", "@type": "Chapter", "description": "An introductory chapter on The Republic.", "title": "The Introduction" }] }
Developers typically like to operate on items in a hierarchical, tree-based fashion. Ideally, a developer would want the data above sorted into top-level libraries, then the books that are contained in each library, and then the chapters contained in each book. To achieve that layout, the developer can define the following frame:
{ "@context": { "Book": "http://example.org/vocab#Book", "Chapter": "http://example.org/vocab#Chapter", "contains": "http://example.org/vocab#contains", "creator": "http://purl.org/dc/terms/creator" "description": "http://purl.org/dc/terms/description" "Library": "http://example.org/vocab#Library", "title": "http://purl.org/dc/terms/title" }, "@type": "Library", "contains": { "@type": "Book", "contains": { "@type": "Chapter" } } }
When the framing algorithm is run against the previously defined JSON-LD document, paired with the frame above, the following JSON-LD document is the end result:
{ "@context": { "Book": "http://example.org/vocab#Book", "Chapter": "http://example.org/vocab#Chapter", "contains": "http://example.org/vocab#contains", "creator": "http://purl.org/dc/terms/creator" "description": "http://purl.org/dc/terms/description" "Library": "http://example.org/vocab#Library", "title": "http://purl.org/dc/terms/title" }, "@subject": "http://example.org/library", "@type": "Library", "contains": { "@subject": "http://example.org/library/the-republic", "@type": "Book", "creator": "Plato", "title": "The Republic", "contains": { "@subject": "http://example.org/library/the-republic#introduction", "@type": "Chapter", "description": "An introductory chapter on The Republic.", "title": "The Introduction" }, }, }
The JSON-LD framing algorithm allows developers to query by example and force a specific tree layout to a JSON-LD document.
JSON-LD has a number of features that provide functionality above and beyond the core functionality described above. The following sections outline the features that are specific to JSON-LD.
Concepts in Linked Data documents may draw on a number of different vocabularies. The @vocab mechanism is useful to easily associate types and properties with a specific vocabulary, but when many vocabularies are used, this becomes difficult. Consider the following example:
{ "@context": { "dc": "http://purl.org/dc/elements/1.1/", "ex": "http://example.org/vocab#" }, "@subject": "http://example.org/library", "@type": "ex:Library", "ex:contains": { "@subject": "http://example.org/library/the-republic", "@type": "ex:Book", "dc:creator": "Plato", "dc:title": "The Republic", "ex:contains": { "@subject": "http://example.org/library/the-republic#introduction", "@type": "ex:Chapter", "dc:description": "An introductory chapter on The Republic.", "dc:title": "The Introduction" }, }, }
In this example, two different vocabularies are identified with prefixes, and used as type and property values using the CURIE notation.
A CURIE is a compact way of describing an IRI. The term actually comes from Compact URI. Generally, a CURIE is composed of a prefix and a suffix separated by a ':'. In JSON-LD, the prefix may be the empty string, denoting the default prefix.
CURIEs are defined more formally in [RDFA-CORE] section 6 "CURIE Syntax Definition".
Since JSON is capable of expressing typed information such as doubles, integers, and boolean values. As demonstrated below, JSON-LD utilizes that information to create typed literals:
{ ... // The following two values are automatically converted to a type of xsd:double // and both values are equivalent to each other. "measure:cups": 5.3, "measure:cups": 5.3e0, // The following value is automatically converted to a type of xsd:double as well "space:astronomicUnits": 6.5e73, // The following value should never be converted to a language-native type "measure:stones": { "@literal": "4.8", "@datatype": "xsd:decimal" }, // This value is automatically converted to having a type of xsd:integer "chem:protons": 12, // This value is automatically converted to having a type of xsd:boolean "sensor:active": true, ... }
When dealing with a number of modern programming languages,
including JavaScript ECMA-262, there is no distinction between
xsd:decimal and xsd:double values. That is,
the number 5.3
and the number
5.3e0
are treated as if they were the same. When converting from
JSON-LD to a language-native format and back, datatype information is lost in a
number of these languages. Thus, one could say that 5.3
is a
xsd:decimal and 5.3e0
is an
xsd:double in JSON-LD, but when both values are
converted to a language-native format the datatype difference between the two
is lost because the machine-level representation will almost always be a
double.
Implementers should be aware of this potential round-tripping issue between
xsd:decimal and xsd:double. Specifically
objects with a datatype of xsd:decimal must not be converted
to a language native type.
JSON-LD supports the coercion of values to particular data types. Type coercion allows someone deploying JSON-LD to coerce the incoming or outgoing types to the proper data type based on a mapping of data type IRIs to property types. Using type coercion, one may convert simple JSON data to properly typed RDF data.
The example below demonstrates how a JSON-LD author can coerce values to plain literals, typed literals and IRIs.
{ "@context": { "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", "xsd": "http://www.w3.org/2001/XMLSchema#", "name": "http://xmlns.com/foaf/0.1/name", "age": "http://xmlns.com/foaf/0.1/age", "homepage": "http://xmlns.com/foaf/0.1/homepage", "@coerce": { "xsd:integer": "age", "@iri": "homepage" } }, "name": "John Smith", "age": "41", "homepage": "http://example.org/home/" }
The example above would generate the following triples:
_:bnode1 <http://xmlns.com/foaf/0.1/name> "John Smith" . _:bnode1 <http://xmlns.com/foaf/0.1/age> "41"^^http://www.w3.org/2001/XMLSchema#integer . _:bnode1 <http://xmlns.com/foaf/0.1/homepage> <http://example.org/home/> .
Object chaining is a JSON-LD feature that allows an author to use the definition of JSON-LD objects as property values. This is a commonly used mechanism for creating a parent-child relationship between two subjects.
The example shows an two subjects related by a property from the first subject:
{ ... "name": "Manu Sporny", "knows": { "@type": "Person", "name": "Gregg Kellogg", } ... }
An object definition, like the one used above, may be used as a JSON value at any point in JSON-LD.
At times, it becomes necessary to be able to express information without
being able to specify the subject. Typically, this type of node is called
an unlabeled node or a blank node. In JSON-LD, unlabeled node identifiers are
automatically created if a subject is not specified using the
@subject
keyword. However, authors may provide identifiers for
unlabeled nodes by using the special _
(underscore) CURIE
prefix.
{
...
"@subject": "_:foo",
...
}
The example above would set the subject to _:foo
, which can
then be used later on in the JSON-LD markup to refer back to the
unlabeled node. This practice, however, is usually frowned upon when
generating Linked Data. If a developer finds that they refer to the unlabeled
node more than once, they should consider naming the node using a resolve-able
IRI.
JSON-LD allows all of the syntax keywords, except for @context
,
to be overridden. This feature allows more legacy JSON content to be supported
by JSON-LD. It also allows developers to design domain-specific implementations
using only the JSON-LD context.
{ "@context": { "url": "@subject", "a": "@type", "name": "http://schema.org/name" }, "url": "http://example.com/about#gregg", "a": "http://schema.org/Person", "name": "Gregg Kellogg" }
In the example above, the @subject
and @type
keywords have been overridden by url and
a, respectively.
Normalization is the process of taking JSON-LD input and performing a deterministic transformation on that input that results in a JSON-LD output that any conforming JSON-LD processor would have generated given the same input. The problem is a fairly difficult technical problem to solve because it requires a directed graph to be ordered into a set of nodes and edges in a deterministic way. This is easy to do when all of the nodes have unique names, but very difficult to do when some of the nodes are not labeled.
Normalization is useful when comparing two graphs against one another, when generating a detailed list of differences between two graphs, and when generating a cryptographic digital signature for information contained in a graph or when generating a hash of the information contained in a graph.
The example below is an un-normalized JSON-LD document:
{ "name": "Manu Sporny", "homepage": "http://manu.sporny.org/", "@context": { "name": "http://xmlns.com/foaf/0.1/name", "homepage": "http://xmlns.com/foaf/0.1/homepage", "xsd": "http://www.w3.org/2001/XMLSchema#", "@coerce": { "@iri": ["homepage"] } } }
The example below is the normalized form of the JSON-LD document above:
Whitespace is used below to aid readability. The normalization algorithm for JSON-LD remove all unnecessary whitespace in the fully normalized form.
[{ "@subject": { "@iri": "_:c14n0" }, "http://xmlns.com/foaf/0.1/homepage": { "@iri": "http://manu.sporny.org/" }, "http://xmlns.com/foaf/0.1/name": "Manu Sporny" }]
Notice how all of the terms have been expanded and sorted in alphabetical order. Also, notice how the subject has been labeled with a blank node identifier. Normalization ensures that any arbitrary graph containing exactly the same information would be normalized to exactly the same form shown above.
This API provides a clean mechanism that enables developers to convert JSON-LD data into a a variety of output formats that are easier to work with in various programming languages. If an API is provided in a programming environment, the entire API must be implemented.
[NoInterfaceObject]
interface JSONLDProcessor {
object expand (in object input, in optional JSONLDProcessorCallback
? callback);
object compact (in object input, in object context, in optional JSONLDProcessorCallback
? callback);
object frame (in object input, in object frame, in object options, in optional JSONLDProcessorCallback
? callback);
object normalize (in object input, in optional JSONLDProcessorCallback
? callback);
object triples (in object input, in JSONLDTripleCallback
tripleCallback, in optional JSONLDProcessorCallback
? parserCallback);
};
compact
input
according to the steps in the
Compaction Algorithm. The
input
must be copied, compacted and returned if there are
no errors. If the compaction fails, null
must be returned.
Parameter | Type | Nullable | Optional | Description |
---|---|---|---|---|
input | object | ✘ | ✘ | The JSON-LD object to perform compaction on. |
context | object | ✘ | ✘ | The base context to use when compacting the input . |
callback |
| ✔ | ✔ | A callback that is called whenever a processing error occurs on
the given input . |
object
expand
input
according to the steps in the
Expansion Algorithm. The
input
must be copied, expanded and returned if there are
no errors. If the expansion fails, null
must be returned.
Parameter | Type | Nullable | Optional | Description |
---|---|---|---|---|
input | object | ✘ | ✘ | The JSON-LD object to copy and perform the expansion upon. |
callback |
| ✔ | ✔ | A callback that is called whenever a processing error occurs on
the input . |
object
frame
input
using the frame
according to the steps in the
Framing Algorithm. The
input
is used to build the framed output and is returned if
there are no errors. Exceptions are thrown if there are errors.
Parameter | Type | Nullable | Optional | Description |
---|---|---|---|---|
input | object | ✘ | ✘ | The JSON-LD object to perform framing on. |
frame | object | ✘ | ✘ | The frame to use when re-arranging the data. |
options | object | ✘ | ✘ | A set of options that will affect the framing algorithm. |
callback |
| ✔ | ✔ | A callback that is called whenever a processing error occurs on
the given input . |
object
normalize
input
according to the steps in the
Normalization Algorithm. The
input
must be copied, normalized and returned if there are
no errors. If the compaction fails, null
must be returned.
Parameter | Type | Nullable | Optional | Description |
---|---|---|---|---|
input | object | ✘ | ✘ | The JSON-LD object to perform normalization upon. |
callback |
| ✔ | ✔ | A callback that is called whenever a processing error occurs on the given JSON-LD string. |
object
triples
input
according to the
RDF Conversion Algorithm, calling
the provided tripleCallback
for each triple generated.
Parameter | Type | Nullable | Optional | Description |
---|---|---|---|---|
input | object | ✘ | ✘ | The JSON-LD object to process when outputting triples. |
tripleCallback |
| ✘ | ✘ | A callback that is called whenever a processing error occurs on
the given input .
This callback should be aligned with the
RDF API. |
parserCallback |
| ✔ | ✔ | A callback that is called whenever a processing error occurs on
the given input . |
object
The JSONLDProcessorCallback is called whenever a processing error occurs while processing the JSON-LD input.
[NoInterfaceObject Callback]
interface JSONLDProcessorCallback {
void error (in DOMString error);
};
The JSONLDTripleCallback is called whenever the processor generates a
triple during the triple()
call.
[NoInterfaceObject Callback]
interface JSONLDTripleCallback {
void triple (in DOMString subject, in DOMString property, in DOMString objectType, in DOMString object, in DOMString? datatype, in DOMString? language);
};
triple
Parameter | Type | Nullable | Optional | Description |
---|---|---|---|---|
subject | DOMString | ✘ | ✘ | The subject IRI that is associated with the triple. |
property | DOMString | ✘ | ✘ | The property IRI that is associated with the triple. |
objectType | DOMString | ✘ | ✘ | The type of object that is associated with the triple. Valid values
are IRI and literal . |
object | DOMString | ✘ | ✘ | The object value associated with the subject and the property. |
datatype | DOMString | ✔ | ✘ | The datatype associated with the object. |
language | DOMString | ✔ | ✘ | The language associated with the object in BCP47 format. |
void
All algorithms described in this section are intended to operate on language-native data structures. That is, the serialization to a text-based JSON document isn't required as input or output to any of these algorithms and language-native data structures must be used where applicable.
JSON-LD specifies a number of syntax tokens and keywords that are using in all algorithms described in this section:
@context
@base
@vocab
@coerce
@literal
@iri
@language
@datatype
:
@subject
@type
@context
keyword.
Processing of JSON-LD data structure is managed recursively. During processing, each rule is applied using information provided by the active context. Processing begins by pushing a new processor state onto the processor state stack and initializing the active context with the initial context. If a local context is encountered, information from the local context is merged into the active context.
The active context is used for expanding keys and values of a JSON object (or elements of a list (see List Processing)).
A local context is identified within a JSON object having a key of
@context
with string or a JSON object value. When processing a local
context, special processing rules apply:
@base
key, it must have a value of a simple
string with the lexical form of an absolute IRI. Add the base mapping to the local
context. Turtle allows @base to be relative. If we did this, we would have to add IRI Expansion.
@vocab
key, it must have a value of a simple
string with the lexical form of an absolute IRI. Add the vocabulary mapping to the
local context after performing IRI Expansion on
the associated value.
@coerce
key, it must have a value of a
JSON object. Add the @coerce
mapping to the local context
performing IRI Expansion on the associated value(s).
@coerce
mapping into the
active context's @coerce
mapping as described below.
@coerce
mapping from the local contextto the
active context overwriting any duplicate values.
Map each key-value pair in the local context's
@coerce
mapping into the active context's
@coerce
mapping, overwriting any duplicate values in
the active context's @coerce
mapping.
The @coerce
mapping has either a single CURIE or an
array of CURIEs. When merging with an existing mapping in the active context,
map all CURIE values to array form and replace with the union of the value from
the local context and the value of the active context. If the result is an array
with a single CURIE, the processor may represent this as a string value.
The initial context is initialized as follows:
@base
is set using @coerce
is set with a single mapping from @iri
to @type
.
{
"@base": document-location,
"@context": {
"@iri": "@type"
}
}
Keys and some values are evaluated to produce an IRI. This section defines an algorithm for transforming a value representing an IRI into an actual IRI.
IRIs may be represented as an explicit string, or as a CURIE, as a value relative to @base
or @vocab
.
The algorithm for generating an IRI is:
@coerce
mapping) and the active context has a @vocab
mapping,
join the mapped value to the suffix using textual concatenation.@base
mapping,
join the mapped value to the suffix using the method described in [RFC3986].Some keys and values are expressed using IRIs. This section defines an algorithm for transforming an IRI to a compact IRI using the terms and prefixes specified in the local context.
The algorithm for generating a compacted IRI is:
Some values in JSON-LD can be expressed in a compact form. These values are required to be expanded at times when processing JSON-LD documents.
The algorithm for expanding a value is:
@iri
, expand the value
by adding a new key-value pair where the key is @iri
and the value is the expanded IRI according to the
IRI Expansion rules.@literal
and the unexpanded value. The second
key-value pair will be @datatype
and the associated
coercion datatype expanded according to the
IRI Expansion rules.Some values, such as IRIs and typed literals, may be expressed in an expanded form in JSON-LD. These values are required to be compacted at times when processing JSON-LD documents.
The algorithm for compacting a value is:
@iri
, the compacted
value is the value associated with the @iri
key,
processed according to the
IRI Compaction steps.@literal
key.
This algorithm is a work in progress, do not implement it.
As stated previously, expansion is the process of taking a JSON-LD input and expanding all IRIs and typed literals to their fully-expanded form. The output will not contain a single context declaration and will have all IRIs and typed literals fully expanded.
This algorithm is a work in progress, do not implement it.
As stated previously, compaction is the process of taking a JSON-LD input and compacting all IRIs using a given context. The output will contain a single top-level context declaration and will only use terms and prefixes and will ensure that all typed literals are fully compacted.
This algorithm is a work in progress, do not implement it.
A JSON-LD document is a representation of a directed graph. A single directed graph can have many different serializations, each expressing exactly the same information. Developers typically don't work directly with graphs, but rather, prefer trees when dealing with JSON. While mapping a graph to a tree can be done, the layout of the end result must be specified in advance. This section defines an algorithm for mapping a graph to a tree given a frame.
The framing algorithm takes JSON-LD input that has been normalized according to the Normalization Algorithm (normalized input), an input frame that has been expanded according to the Expansion Algorithm (expanded frame), and a number of options and produces JSON-LD output. The following series of steps is the recursive portion of the framing algorithm:
null
.Invalid Frame Format
exception. Add each matching item from the normalized input
to the matches array and decrement the
match limit by 1 if:
rdf:type
that exists in the item's list of rdf:type
s. Note:
the rdf:type
can be an array, but only one value needs
to be in common between the item and the
expanded frame for a match.rdf:type
property, but every property in the
expanded frame exists in the item.@embed
keyword, set the object embed flag to its value.
If the match frame contains an @explicit
keyword, set the explicit inclusion flag to its value.
Note: if the keyword exists, but the value is neither
true
or false
, set the associated flag to
true
.@subject
property, replace the item with the value
of the @subject
property.@subject
property, and its IRI is in the
map of embedded subjects, throw a
Duplicate Embed
exception.@subject
property and its IRI is not in the
map of embedded subjects:
@subject
.rdf:type
:
@iri
value that exists in the
normalized input, replace the object in the
recusion input list with a new object containing
the @subject
key where the value is the value of
the @iri
, and all of the other key-value pairs for
that subject. Set the recursion match frame to the
value associated with the match frame's key. Replace
the value associated with the key by recursively calling this
algorithm using recursion input list,
recursion match frame as input.null
otherwise.null
,
process the omit missing properties flag:
@omitDefault
keyword, set the
omit missing properties flag to its value.
Note: if the keyword exists, but the value is neither
true
or false
, set the associated
flag to true
.@default
keyword is set in the
property frame set the item's value to the value
of @default
.null
set it to
the item, otherwise, append the item to the
JSON-LD output.
This algorithm is a work in progress, do not implement it.
Normalization is the process of taking JSON-LD input and performing a deterministic transformation on that input that results in all aspects of the graph being fully expanded and named in the JSON-LD output. The normalized output is generated in such a way that any conforming JSON-LD processor will generate identical output given the same input. The problem is a fairly difficult technical problem to solve because it requires a directed graph to be ordered into a set of nodes and edges in a deterministic way. This is easy to do when all of the nodes have unique names, but very difficult to do when some of the nodes are not labeled.
In time, there may be more than one normalization algorithm that will need to be identified. For identification purposes, this algorithm is named UGNA2011.
@subject
and the value is a string that is an IRI or
a JSON object containing the key @iri
and
a value that is a string that is an IRI.
s
or
c
.
When performing the steps required by the normalization algorithm, it is helpful to track the many pieces of information in a data structure called the normalization state. Many of these pieces simply provide indexes into the graph. The information contained in the normalization state is described below.
_:
and that has a
path, via properties, that starts with the
node reference.
_:
and that has a path, via properties, that ends with
the node reference.
_:
, is not used by any other
node's label in the JSON-LD input, and does not
start with the characters _:c14n
. The prefix has two uses.
First it is used to temporarily name nodes during the normalization
algorithm in a way that doesn't collide with the names that already
exist as well as the names that will be generated by the normalization
algorithm. Second, it will eventually be set to _:c14n
to
generate the final, deterministic labels for nodes in the graph. This
prefix will be concatenated with the labeling counter to
produce a node label. For example, _:j8r3k
is
a proper initial value for the labeling prefix.
1
.
The normalization algorithm expands the JSON-LD input, flattens the data structure, and creates an initial set of names for all nodes in the graph. The flattened data structure is then processed by a node labeling algorithm in order to get a fully expanded and named list of nodes which is then sorted. The result is a deterministically named and ordered list of graph nodes.
@subject
and the value is the
concatenation of the labeling prefix
and the string value of the labeling counter.
Increment the labeling counter.@iri
and the value is
the value of the @subject
key in the node._:c14n
, relabel the node
using the Node Relabeling Algorithm.
@subject
key associated
with a value starting with _:
according to the steps in the
Deterministic Labeling Algorithm.
This algorithm renames a node by generating a unique new label and updating all references to that label in the node state map. The old label and the normalization state must be given as an input to the algorithm. The old label is the current label of the node that is to be relabeled.
The node relabeling algorithm is as follows:
_:c14n
and the
old label begins with _:c14n
then return as
the node has already been renamed.
The deterministic labeling algorithm takes the normalization state and produces a list of finished nodes that is sorted and contains deterministically named and expanded nodes from the graph.
_:c14n
, the
labeling counter to 1
,
the list of finished nodes to an empty array, and create
an empty array, the list of unfinished nodes._:
then put the node reference in the
list of finished nodes.
_:
then put the node reference in the
list of unfinished nodes.
_:c14n
from the list of unfinished nodes and
add it to the list of finished nodes.
The shallow comparison algorithm takes two unlabeled nodes, alpha and beta, as input and determines which one should come first in a sorted list. The following algorithm determines the steps that are executed in order to determine the node that should come first in a list:
The object comparison algorithm is designed to compare two graph node property values, alpha and beta, against the other. The algorithm is useful when sorting two lists of graph node properties.
@literal
is first.@datatype
is first.@language
is first.@iri
is first.The deep comparison algorithm is used to compare the difference between two nodes, alpha and beta. A deep comparison takes the incoming and outgoing node edges in a graph into account if the number of properties and value of those properties are identical. The algorithm is helpful when sorting a list of nodes and will return whichever node should be placed first in a list if the two nodes are not truly equivalent.
When performing the steps required by the deep comparison algorithm, it is helpful to track state information about mappings. The information contained in a mapping state is described below.
1
.
s1
and its
index is set to 0
.
The deep comparison algorithm is as follows:
outgoing direction
to the algorithm as inputs.
outgoing direction
to the algorithm as inputs.
incoming direction
to the algorithm as inputs.
incoming direction
to the algorithm as inputs.
The node serialization algorithm takes a node state, a
mapping state, and a direction (either
outgoing direction
or incoming direction
) as
inputs and generates a deterministic serialization for the
node reference.
true
.
outgoing direction
and the
incoming list otherwise, if the label starts with
_:
, it is the target node label:
1
or the length of the
list of unserialized labels, whichever is greater.0
, perform the
Combinatorial Serialization Algorithm
and decrement the maximum serialization combinations by
1
for each iteration.
The algorithm generates a serialization label given a label and a mapping count.
_:c14n
,
the serialization label is the letter c
followed by the number that follows _:c14n
in the
label.s
followed by the string value of
mapping count. Increment the mapping count by
1
ensuring that the value persists across multiple
invocations of this algorithm.SerializeCombos() takes a label, a serialization map, a serialization label, a processed labels map, a serialization map, a serialized labels map, and a list of unserialized labels as inputs and generates deterministic serializations for all possible combinations of graphs.
1
or the length of the
list of unserialized labels, whichever is greater.0
:
1
for each iteration.
map of all labels, map of all properties, key stack, serialization string
SerializeMapping(mapping): (This function incrementally updates the relation serialization for a mapping)
_
character and the serialization key to the
serialization string.
true
.
map of properties, label serialization, label, incoming map, adjacent node labels, key stack.
[
character to the
label serialization.@subject
property ???do the map keys need to be sorted???:
<
KEY>
where KEY is the current key. Append string to the
label serialization.@iri
key with a
value that starts
with _:
, set the object string to
the value _:
. If the value does not
start with _:
, build the object string
using the pattern
<
IRI>
where IRI is the value associated with the
@iri
key.@literal
key and a
@datatype
key, build the object string
using the pattern
"
LITERAL"^^<
DATATYPE>
where LITERAL is the value associated with the
@literal
key and DATATYPE is the
value associated with the @datatype
key.@literal
key and a
@language
key, build the object string
using the pattern
"
LITERAL"@
LANGUAGE
where LITERAL is the value associated with the
@literal
key and LANGUAGE is the
value associated with the @language
key."
LITERAL"
where LITERAL is the value associated with the
current key.|
separator character to the
label serialization.]
character to the
label serialization.[
character to the
label serialization.<
PROPERTY>
<
REFERER>
where PROPERTY is the property associated with the
incoming reference and REFERER is either the subject of
the node referring to the label in the incoming reference
or _:
if REFERER begins with
_:
.
|
separator character to the
label serialization.]
character to the
label serialization.When normalizing xsd:double values, implementers must
ensure that the normalized value is a string. In order to generate the
string from a double value, output equivalent to the
printf("%1.6e", value)
function in C must be used where
"%1.6e" is the string formatter and value
is the value to be converted.
To convert the a double value in JavaScript, implementers can use the following snippet of code:
// the variable 'value' below is the JavaScript native double value that is to be converted (value).toExponential(6).replace(/(e(?:\+|-))([0-9])$/, '$10$2')
When data needs to be normalized, JSON-LD authors should not use values that are going to undergo automatic conversion. This is due to the lossy nature of xsd:double values.
Round-tripping data can be problematic if we mix and match @coerce rules with JSON-native datatypes, like integers. Consider the following code example:
var myObj = { "@context" : { "number" : "http://example.com/vocab#number", "@coerce": { "xsd:nonNegativeInteger": "number" } }, "number" : 42 }; // Map the language-native object to JSON-LD var jsonldText = jsonld.normalize(myObj); // Convert the normalized object back to a JavaScript object var myObj2 = jsonld.parse(jsonldText);
At this point, myObj2 and myObj will have different values for the "number" value. myObj will be the number 42, while myObj2 will be the string "42". This type of data round-tripping error can bite developers. We are currently wondering if having a "coerce validation" phase in the parsing/normalization phases would be a good idea. It would prevent data round-tripping issues like the one mentioned above.
A JSON-LD document may be converted to any other RDF-compatible document format using the algorithm specified in this section.
The JSON-LD Processing Model describes processing rules for extracting RDF from a JSON-LD document. Note that many uses of JSON-LD may not require generation of RDF.
The processing algorithm described in this section is provided in order to demonstrate how one might implement a JSON-LD to RDF processor. Conformant implementations are only required to produce the same type and number of triples during the output process and are not required to implement the algorithm exactly as described.
The RDF Conversion Algorithm is a work in progress.
This section is non-normative.
JSON-LD is intended to have an easy to parse grammar that closely models existing practice in using JSON for describing object representations. This allows the use of existing libraries for parsing JSON in a document-oriented fashion, or can allow for stream-based parsing similar to SAX.
As with other grammars used for describing Linked Data, a key concept is that of a resource. Resources may be of three basic types: IRIs, for describing externally named entities, BNodes, resources for which an external name does not exist, or is not known, and Literals, which describe terminal entities such as strings, dates and other representations having a lexical representation possibly including an explicit language or datatype.
Data described with JSON-LD may be considered to be the representation of a graph made up of subject and object resources related via a property resource. However, specific implementations may choose to operate on the document as a normal JSON description of objects having attributes.
The algorithm below is designed for in-memory implementations with random access to JSON object elements.
A conforming JSON-LD processor implementing RDF conversion must implement a processing algorithm that results in the same default graph that the following algorithm generates:
@context
key, process the local context as
described in Context.
@iri
key, set the active object by
performing IRI Expansion on the associated value. Generate a
triple representing the active subject, the active property and the
active object. Return the active object to the calling location.
@iri
really just behaves the same as @subject
, consider consolidating them.
@literal
key, set the active object
to a literal value as follows:
@datatype
key
after performing IRI Expansion on the specified@datatype
.
@language
key, use it's value to set the language of the plain literal.
@subject
key:
@subject
key, set the active
object to newly generated blank node identifier. Generate a triple
representing the active subject, the active property and the
active object. Set the active subject to the active
object.
@type
, set the active property
to rdf:type
.
@iri
coercion,
set the active object by
performing IRI Expansion on the string.
xsd:integer
or
xsd:double
, depending on if the value contains a
fractional and/or an exponential component. Generate a triple using the active
subject, active property and the generated typed literal.
xsd:boolean
.
There are a few advanced concepts where it is not clear whether or not the JSON-LD specification is going to support the complexity necessary to support each concept. The entire section on Advanced Concepts should be considered as discussion points; it is merely a list of possibilities where all of the benefits and drawbacks have not been explored.
When serializing an RDF graph that contains two or more sections of the graph which are entirely disjoint, one must use an array to express the graph as two graphs. This may not be acceptable to some authors, who would rather express the information as one graph. Since, by definition, disjoint graphs require there to be two top-level objects, JSON-LD utilizes a mechanism that allows disjoint graphs to be expressed using a single graph.
Assume the following RDF graph:
<http://example.org/people#john> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> . <http://example.org/people#jane> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://xmlns.com/foaf/0.1/Person> .
Since the two subjects are entirely disjoint with one another, it is impossible to express the RDF graph above using a single JSON object.
In JSON-LD, one can use the subject to express disjoint graphs as a single graph:
{ "@context": { "Person": "http://xmlns.com/foaf/0.1/Person" }, "@subject": [ { "@subject": "http://example.org/people#john", "@type": "Person" }, { "@subject": "http://example.org/people#jane", "@type": "Person" } ] }
A disjoint graph could also be expressed like so:
[ { "@subject": "http://example.org/people#john", "@type": "http://xmlns.com/foaf/0.1/Person" }, { "@subject": "http://example.org/people#jane", "@type": "http://xmlns.com/foaf/0.1/Person" } ]
Because graphs do not describe ordering for links between nodes, multi-valued properties in JSON do not provide an ordering of the listed objects. For example, consider the following simple document:
{
...
"@subject": "http://example.org/people#joebob",
"nick": ["joe", "bob", "jaybee"],
...
}
This results in three triples being generated, each relating the subject to an individual
object, with no inherent order. To address this issue, RDF-based languages, such as [TURTLE]
use the concept of an rdf:List
(as described in [RDF-SCHEMA]). This uses a sequence
of unlabeled nodes with properties describing a value, a null-terminated next property. Without
specific syntactical support, this could be represented in JSON-LD as follows:
{ ... "@subject": "http://example.org/people#joebob", "nick": {, "@first": "joe", "@rest": { "@first": "bob", "@rest": { "@first": "jaybee", "@rest": "@nil" } } } }, ... }
As this notation is rather unwieldy and the notion of ordered collections is rather important
in data modeling, it is useful to have specific language support. In JSON-LD, a list may
be represented using the @list
keyword as follows:
{
...
"@subject": "http://example.org/people#joebob",
"foaf:nick": {"@list": ["joe", "bob", "jaybee"]},
...
}
This describes the use of this array as being ordered, and order is maintained through
normalization and RDF conversion. If every use of a given multi-valued property is a
list, this may be abbreviated by adding an @coerce
term:
{ "@context": { ... "@context": { "@list": ["foaf:nick"] } }, ... "@subject": "http://example.org/people#joebob", "foaf:nick": ["joe", "bob", "jaybee"], ... }
TBD.
TBD.
To support RDF Conversion of lists, RDF Conversion Algorithm is updated as follows:
@list
key and the value is an array
process the value as a list starting at Step 3a.
@list
coercion,
and the value is an array,
process the value as a list starting at Step 3a.
rdf:first
and rdf:next
, terminating the list with rdf:nil
using the following sequence:
rdf:nil
.
rdf:first
as the active property.rdf:nil
.rdf:rest
and rest blank node identifier.The JSON-LD markup examples below demonstrate how JSON-LD can be used to express semantic data marked up in other languages such as RDFa, Microformats, and Microdata. These sections are merely provided as proof that JSON-LD is very flexible in what it can express across different Linked Data approaches.
The following example describes three people with their respective names and homepages.
<div prefix="foaf: http://xmlns.com/foaf/0.1/"> <ul> <li typeof="foaf:Person"> <a rel="foaf:homepage" href="http://example.com/bob/" property="foaf:name" >Bob</a> </li> <li typeof="foaf:Person"> <a rel="foaf:homepage" href="http://example.com/eve/" property="foaf:name" >Eve</a> </li> <li typeof="foaf:Person"> <a rel="foaf:homepage" href="http://example.com/manu/" property="foaf:name" >Manu</a> </li> </ul> </div>
An example JSON-LD implementation is described below, however, there are other ways to mark-up this information such that the context is not repeated.
{ "@context": { "foaf": "http://xmlns.com/foaf/0.1/"}, "@subject": [ { "@subject": "_:bnode1", "@type": "foaf:Person", "foaf:homepage": "http://example.com/bob/", "foaf:name": "Bob" }, { "@subject": "_:bnode2", "@type": "foaf:Person", "foaf:homepage": "http://example.com/eve/", "foaf:name": "Eve" }, { "@subject": "_:bnode3", "@type": "foaf:Person", "foaf:homepage": "http://example.com/manu/", "foaf:name": "Manu" } ] }
The following example uses a simple Microformats hCard example to express how the Microformat is represented in JSON-LD.
<div class="vcard"> <a class="url fn" href="http://tantek.com/">Tantek Çelik</a> </div>
The representation of the hCard expresses the Microformat terms in the
context and uses them directly for the url
and fn
properties. Also note that the Microformat to JSON-LD processor has
generated the proper URL type for http://tantek.com
.
{ "@context": { "vcard": "http://microformats.org/profile/hcard#vcard", "url": "http://microformats.org/profile/hcard#url", "fn": "http://microformats.org/profile/hcard#fn", "@coerce": { "xsd:anyURI": "url" } }, "@subject": "_:bnode1", "@type": "vcard", "url": "http://tantek.com/", "fn": "Tantek Çelik" }
The Microdata example below expresses book information as a Microdata Work item.
<dl itemscope itemtype="http://purl.org/vocab/frbr/core#Work" itemid="http://purl.oreilly.com/works/45U8QJGZSQKDH8N"> <dt>Title</dt> <dd><cite itemprop="http://purl.org/dc/terms/title">Just a Geek</cite></dd> <dt>By</dt> <dd><span itemprop="http://purl.org/dc/terms/creator">Wil Wheaton</span></dd> <dt>Format</dt> <dd itemprop="http://purl.org/vocab/frbr/core#realization" itemscope itemtype="http://purl.org/vocab/frbr/core#Expression" itemid="http://purl.oreilly.com/products/9780596007683.BOOK"> <link itemprop="http://purl.org/dc/terms/type" href="http://purl.oreilly.com/product-types/BOOK"> Print </dd> <dd itemprop="http://purl.org/vocab/frbr/core#realization" itemscope itemtype="http://purl.org/vocab/frbr/core#Expression" itemid="http://purl.oreilly.com/products/9780596802189.EBOOK"> <link itemprop="http://purl.org/dc/terms/type" href="http://purl.oreilly.com/product-types/EBOOK"> Ebook </dd> </dl>
Note that the JSON-LD representation of the Microdata information stays true to the desires of the Microdata community to avoid contexts and instead refer to items by their full IRI.
[ { "@subject": "http://purl.oreilly.com/works/45U8QJGZSQKDH8N", "@type": "http://purl.org/vocab/frbr/core#Work", "http://purl.org/dc/terms/title": "Just a Geek", "http://purl.org/dc/terms/creator": "Whil Wheaton", "http://purl.org/vocab/frbr/core#realization": ["http://purl.oreilly.com/products/9780596007683.BOOK", "http://purl.oreilly.com/products/9780596802189.EBOOK"] }, { "@subject": "http://purl.oreilly.com/products/9780596007683.BOOK", "@type": "http://purl.org/vocab/frbr/core#Expression", "http://purl.org/dc/terms/type": "http://purl.oreilly.com/product-types/BOOK" }, { "@subject": "http://purl.oreilly.com/products/9780596802189.EBOOK", "@type": "http://purl.org/vocab/frbr/core#Expression", "http://purl.org/dc/terms/type": "http://purl.oreilly.com/product-types/EBOOK" } ]
Developers would also benefit by allowing other vocabularies to be used automatically with their JSON API. There are over 200 Vocabulary Documents that are available for use on the Web today. Some of these vocabularies are:
You can use these vocabularies in combination, like so:
{ "@type": "foaf:Person", "foaf:name": "Manu Sporny", "foaf:homepage": "http://manu.sporny.org/", "sioc:avatar": "http://twitter.com/account/profile_image/manusporny" }
Developers can also specify their own Vocabulary documents by modifying the
active context in-line using the @context
keyword,
like so:
{ "@context": { "myvocab": "http://example.org/myvocab#" }, "@type": "foaf:Person", "foaf:name": "Manu Sporny", "foaf:homepage": "http://manu.sporny.org/", "sioc:avatar": "http://twitter.com/account/profile_image/manusporny", "myvocab:personality": "friendly" }
The @context
keyword is used to change how the JSON-LD
processor evaluates key-value pairs. In this case, it was used to
map one string ('myvocab') to another string, which is interpreted as
a IRI. In the example above, the myvocab
string is replaced
with "http://example.org/myvocab#
" when it
is detected. In the example above, "myvocab:personality
" would
expand to "http://example.org/myvocab#personality
".
This mechanism is a short-hand for RDF, called a CURIE, and provides developers an unambiguous way to map any JSON value to RDF.
The editors would like to thank Mark Birbeck, who provided a great deal of the initial push behind the JSON-LD work via his work on RDFj, Dave Longley, Dave Lehn and Mike Johnson who reviewed, provided feedback, and performed several implementations of the specification, and Ian Davis, who created RDF/JSON. Thanks also to Nathan Rixham, Bradley P. Allen, Kingsley Idehen, Glenn McDonald, Alexandre Passant, Danny Ayers, Ted Thibodeau Jr., Olivier Grisel, Niklas Lindström, Markus Lanthaler, and Richard Cyganiak for their input on the specification. Another huge thank you goes out to Dave Longley who designed many of the algorithms used in this specification, including the normalization algorithm which was a monumentally difficult design challenge.