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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 creating a graph of interlinked data across different documents or Web sites. Data entities are described using IRIs, which are typically dereferencable and thus may be used to find more information about an entity, creating a "Web of Knowledge". JSON-LD is intended to be a simple publishing method for expressing not only Linked Data in JSON, but also 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 use 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 libraries available 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 necessarily require applications to change their JSON, but allows to 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 upgrade path from JSON to JSON with added semantics. Finally, the format is intended to be easy to parse, efficient 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 Linked Data in JSON. 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 prefixes to refer to Web Vocabularies. The following is a list of all vocabularies and their prefix 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 terms, i.e., keys and 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 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 map terms, i.e., keys and values
in an JSON document, to
IRIs. A term is a short word that may be expanded
to an IRI. The Web uses IRIs for unambiguous identification. The
idea is that these terms mean something that may be of use to
other developers and that it is useful to give them an unambiguous identifier.
That is, it is useful for terms to expand to IRIs so that
developers don't accidentally step on each other's Web Vocabulary terms.
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 page, API or database in which it
resides.
These Linked Data terms are typically collected in a context document that 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"
}
This context document can then be used in an JSON-LD document by adding a single line. The JSON markup as shown in the previous section could be changed as follows to link to the context document:
{
"@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. 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.
{
"@context": {
"name": "http://xmlns.com/foaf/0.1/name",
"homepage": "http://xmlns.com/foaf/0.1/homepage",
"avatar": "http://xmlns.com/foaf/0.1/avatar"
},
"name": "Manu Sporny",
"homepage": "http://manu.sporny.org/",
"avatar": "http://twitter.com/account/profile_image/manusporny"
}
JSON-LD strives to ensure that developers don't have to change the JSON that is going into and being returned from their Web APIs. This means that developers can also specify a context for JSON data in an out-of-band fashion. This is described later in this document.
JSON-LD uses a special type of machine-readable document called a Web Vocabulary to define terms that are then used to describe concepts and "things" in the world. 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 base 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 dozens of 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 could 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 definition 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.
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 to drastically change their workflow.
Please note that this JSON-LD document doesn't define the subject and will thus result in an unlabeled or blank node.
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": {"foaf": "http://xmlns.com/foaf/0.1/"},
...
"foaf:name": "Manu Sporny",
...
}
foaf: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
To be able to externally reference nodes, it is important that each node has an unambiguous identifier. 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 the node 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 an object in JSON 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-iri).
To be Linked Data, types must 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 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 as a part of Framing or Normalization.
For example, assume the following JSON-LD input document:
{
"@context":
{
"name": "http://xmlns.com/foaf/0.1/name",
"homepage": "http://xmlns.com/foaf/0.1/homepage",
"@coerce":
{
"@iri": "homepage"
}
},
"name": "Manu Sporny",
"homepage": "http://manu.sporny.org/"
}
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:
{
"@context":
{
"name": "http://xmlns.com/foaf/0.1/name",
"homepage": "http://xmlns.com/foaf/0.1/homepage",
"@coerce":
{
"@iri": "homepage"
}
},
"name": "Manu Sporny",
"homepage": "http://manu.sporny.org/"
}
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:
{
"@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",
"@coerce":
{
"@iri": "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.
Vocabulary terms in Linked Data documents may draw from a number of different Web vocabularies. At times, declaring every single term that a document uses can require the developer to declare tens, if not hundreds of potential vocabulary terms that may be used across an application. This is a concern for at least three reasons; the first is the cognitive load on the developer, the second is the serialized size of the context, the third is future-proofing application contexts. In order to address these issues, the concept of a prefix mechanism is introduced.
A prefix is a compact way of expressing a base
IRI to a Web Vocabulary.
Generally, these prefixes are used by concatenating the prefix and
a term separated by a colon (:).
The prefix is a short string that identifies a particular Web vocabulary.
For example, the prefix foaf may be used as a short
hand for the Friend-of-a-Friend Web Vocabulary, which is identified using
the IRI http://xmlns.com/foaf/0.1/. A developer may append any of
the FOAF Vocabulary terms to the end of the prefix to specify a short-hand
version of the full IRI for the vocabulary term. For example,
foaf:name would be expanded out to the IRI
http://xmlns.com/foaf/0.1/name. Instead of having to remember
and type out the entire IRI, the developer can instead use the prefix in
their JSON-LD markup.
The ability to use prefixes reduces the need for developers
to declare every vocabulary term that they intend to use in
the JSON-LD context. This reduces document serialization size because
every vocabulary term need not be declared in the context.
Prefix also
reduce the cognitive load on the developer. It is far easier to
remember foaf:name than it is to remember
http://xmlns.com/foaf/0.1/name. The use of prefixes also
ensures that a context document does not have to be updated in lock-step
with an externally defined Web Vocabulary. Without prefixes, a developer
would need to keep their application context terms in lock-step with an
externally defined Web Vocabulary. Rather, by just declaring the
Web Vocabulary prefix, one can use new terms as they're declared
without having to update the application's JSON-LD context.
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 referred to using
prefixes. Those prefixes are then used as type and property values using
the prefix:term notation.
Prefixes, also known as CURIEs, are defined more formally in RDFa Core 1.1, Section 6 "CURIE Syntax Definition" [RDFA-CORE]. JSON-LD does not support the square-bracketed CURIE syntax as the mechanism is not required to disambiguate IRIs in a JSON-LD document like it is in HTML documents.
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)
prefix. This allows to reference the node locally within the
document but not in an external document.
{
...
"@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 aliased. 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 given the aliases 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:
{
"@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"]
}
},
"name": "Manu Sporny",
"homepage": "http://manu.sporny.org/"
}
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 removes 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 a JSON-LD API is provided in a programming environment, the entirety of the following API must be implemented.
[NoInterfaceObject]
interface JsonLdProcessor {
object expand (object input, optional object? context) raises (InvalidContext);
object compact (object input, optional object? context) raises (InvalidContext, ProcessingError);
object frame (object input, object frame, object options) raises (InvalidFrame);
object normalize (object input, optional object? context) raises (InvalidContext);
object triples (object input, JsonLdTripleCallback tripleCallback, optional object? context) raises (InvalidContext);
};
compactinput
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, an appropirate exception must be
thrown.
| 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. |
| Exception | Description | ||||
|---|---|---|---|---|---|
| InvalidContext |
| ||||
| ProcessingError |
|
objectexpandinput
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, an appropriate exception must be thrown.
| Parameter | Type | Nullable | Optional | Description |
|---|---|---|---|---|
| input | object | ✘ | ✘ | The JSON-LD object to copy and perform the expansion upon. |
| context | object | ✔ | ✔ | An external context to use additionally to the context embedded in input when expanding the input. |
| Exception | Description | ||||
|---|---|---|---|---|---|
| InvalidContext |
|
objectframeinput
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. If there are no matches for the frame,
null must be returned. Exceptions must be 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. |
| Exception | Description | ||||
|---|---|---|---|---|---|
| InvalidFrame |
|
objectnormalizeinput
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. |
| context | object | ✔ | ✔ | An external context to use additionally to the context embedded in input when expanding the input. |
| Exception | Description | ||||
|---|---|---|---|---|---|
| InvalidContext |
|
objecttriplesinput 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. |
| context | object | ✔ | ✔ | An external context to use additionally to the context embedded in input when expanding the input. |
| Exception | Description | ||||
|---|---|---|---|---|---|
| InvalidContext |
|
objectThe JsonLdTripleCallback is called whenever the processor generates a
triple during the triple() call.
[NoInterfaceObject Callback]
interface JsonLdTripleCallback {
void triple (DOMString subject, DOMString property, DOMString objectType, DOMString object, DOMString? datatype, 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. |
voidAll 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
prefix:term value, a single term value or an
array of prefix:term or term values.
When merging with an existing mapping in the active context,
map all prefix and term 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 value, 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 absolute IRI, a term, a prefix:term construct, or 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:types. 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:
_: is first.
_:, then the node associated with the
lexicographically lesser label is first._:c14n is first.
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
adjacent unserialized labels list, whichever is greater.0, perform the
Combinatorial Serialization Algorithm
passing the node state, the mapping state for the
first iteration and a copy of it for each subsequent iteration, the
generated serialization label, the direction,
the adjacent serialized labels map, and the
adjacent unserialized labels list.
Decrement the maximum serialization combinations by
1 for each iteration.
The algorithm generates a serialization label given a label and a mapping state and returns the serialization label.
_: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.
The combinatorial serialization algorithm takes a node state, a mapping state, a serialization label, a direction, a adjacent serialized labels map, and a adjacent unserialized labels list as inputs and generates the lexicographically least serialization of nodes relating to the node reference.
1 or the length of the
adjacent unserialized labels list, whichever is greater.
0:
1 for each iteration.
outgoing direction
then directed serialization refers to the
outgoing serialization and the
directed serialization map refers to the
outgoing serialization map, otherwise it refers to the
incoming serialization and the
directed serialization map refers to the
incoming serialization map. Compare the
serialization string to the
directed serialization according to the
Serialization Comparison Algorithm.
If the serialization string is less than or equal to
the directed serialization:
The serialization comparison algorithm takes two serializations, alpha and beta and returns either which of the two is less than the other or that they are equal.
The mapping serialization algorithm incrementally updates the serialization string in a mapping state.
_ character and the
serialization key to the
serialization string.
true.
0
onto the key stack.
The label serialization algorithm serializes information about a node that has been assigned a particular serialization label.
[ character to the
label serialization.@subject property. The keys should be processed in
lexicographical order and their associated values should be processed
in the order produced by the
Object Comparison Algorithm:
<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.
Some JSON serializers, such as PHP's native implementation,
backslash-escapes the forward slash character. For example, the value
http://example.com/ would be serialized as
http:\/\/example.com\/ in some
versions of PHP. This is problematic when generating a byte
stream for processes such as normalization. There is no need to
backslash-escape forward-slashes in JSON-LD. To aid interoperability between
JSON-LD processors, a JSON-LD serializer must not backslash-escape
forward slashes.
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"
}
]
Warning: Using this serialisation format it is impossible to include @context
given that the document's data structure is an array and not an object.
Because graphs do not describe ordering for links between nodes, in contrast to plain JSON, multi-valued properties in JSON-LD 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 preserve the order of the objects, 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": {
...
"@coerce": {
"@list": ["foaf:nick"]
}
},
...
"@subject": "http://example.org/people#joebob",
"foaf:nick": ["joe", "bob", "jaybee"],
...
}
There is an ongoing discussion about this issue. One of the proposed solutions is allowing to change the default behaviour so that arrays are considered as ordered lists by default.
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": { "@iri": "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 Web 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, called a Web Vocabulary prefix, and provides developers an unambiguous way to map any JSON value to RDF.
This section is included merely for standards community review and will be submitted to the Internet Engineering Steering Group if this specification becomes a W3C Recommendation.
formcompacted, expanded,
framed, and normalized. Other values are
allowed, but must be pre-pended with a x- string until
they are clearly defined by a stable specification. If no form
is specified in an HTTP request header to a responding application,
such as a Web server, the application may choose any form. If no
form is specified for a receiving application, the form must not
be assumed to take any particular form.application/json MIME media type.eval()
function. It is recommended that a conforming parser does not attempt to
directly evaluate the JSON-LD serialization and instead purely parse the
input into a language-native data structure. 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.