JSON2RDF
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Streaming generic JSON to RDF converter
JSON2RDF
Streaming generic JSON to RDF converter
Reads JSON data and streams N-Triples output. The conversion algorithm is similar to that of JSON-LD but accepts arbitrary JSON and does not require a @context.
The resulting RDF representation is lossless with the exception of array ordering and some datatype round-tripping.
The lost ordering should not be a problem in the majority of cases, as RDF applications tend to impose their own value-based ordering using SPARQL ORDER BY.
A common use case is feeding the JSON2RDF output into a triplestore or SPARQL processor and using a SPARQL CONSTRUCT query to map the generic RDF to more specific RDF that uses terms from some vocabulary.
SPARQL is an inherently more flexible RDF mapping mechanism than JSON-LD @context.
Build
mvn clean install
That should produce an executable JAR file target/json2rdf-1.0.1-jar-with-dependencies.jar in which dependency libraries will be included.
Usage
The JSON data is read from stdin, the resulting RDF data is written to stdout.
JSON2RDF is available as a .jar as well as a Docker image atomgraph/json2rdf (recommended).
Parameters:
-
base- the base URI for the data. Property namespace is constructed by adding#to the base URI.
Options:
-
--input-charset- JSON input encoding, by default UTF-8 -
--output-charset- RDF output encoding, by default UTF-8
Examples
JSON2RDF output is streaming and produces N-Triples, therefore we pipe it through riot to get a more readable Turtle output.
Bob DuCharme's blog post on using JSON2RDF: Converting JSON to RDF.
JSON data in ordinary-json-document.json
{
"name": "Markus Lanthaler",
"homepage": "http://www.markus-lanthaler.com/",
"image": "http://twitter.com/account/profile_image/markuslanthaler"
}
Java execution from shell:
cat ordinary-json-document.json | java -jar json2rdf-1.0.1-jar-with-dependencies.jar https://localhost/ | riot --formatted=TURTLE
Alternatively, Docker execution from shell:
cat ordinary-json-document.json | docker run -i -a stdin -a stdout -a stderr atomgraph/json2rdf https://localhost/ | riot --formatted=TURTLE
Note that using Docker you need to bind stdin/stdout/stderr streams.
Turtle output
[ <https://localhost/#homepage> "http://www.markus-lanthaler.com/" ;
<https://localhost/#image> "http://twitter.com/account/profile_image/markuslanthaler" ;
<https://localhost/#name> "Markus Lanthaler"
] .
The following SPARQL query can be used to map this generic RDF to the desired target RDF, e.g. a structure that uses schema.org vocabulary.
BASE <https://localhost/>
PREFIX : <#>
PREFIX schema: <http://schema.org/>
CONSTRUCT
{
?person schema:homepage ?homepage ;
schema:image ?image ;
schema:name ?name .
}
{
?person :homepage ?homepageStr ;
:image ?imageStr ;
:name ?name .
BIND (URI(?homepageStr) AS ?homepage)
BIND (URI(?imageStr) AS ?image)
}
Turtle output after the mapping
[ <http://schema.org/homepage> <http://www.markus-lanthaler.com/> ;
<http://schema.org/image> <http://twitter.com/account/profile_image/markuslanthaler> ;
<http://schema.org/name> "Markus Lanthaler"
] .
JSON data in city-distances.json
{
"desc" : "Distances between several cities, in kilometers.",
"updated" : "2014-02-04T18:50:45",
"uptodate": true,
"author" : null,
"cities" : {
"Brussels": [
{"to": "London", "distance": 322},
{"to": "Paris", "distance": 265},
{"to": "Amsterdam", "distance": 173}
],
"London": [
{"to": "Brussels", "distance": 322},
{"to": "Paris", "distance": 344},
{"to": "Amsterdam", "distance": 358}
],
"Paris": [
{"to": "Brussels", "distance": 265},
{"to": "London", "distance": 344},
{"to": "Amsterdam", "distance": 431}
],
"Amsterdam": [
{"to": "Brussels", "distance": 173},
{"to": "London", "distance": 358},
{"to": "Paris", "distance": 431}
]
}
}
Java execution from shell:
cat city-distances.json | java -jar json2rdf-1.0.1-jar-with-dependencies.jar https://localhost/ | riot --formatted=TURTLE
Alternatively, Docker execution from shell:
cat city-distances.json | docker run -i -a stdin -a stdout -a stderr atomgraph/json2rdf https://localhost/ | riot --formatted=TURTLE
Turtle output
[ <https://localhost/#cities> [ <https://localhost/#Amsterdam> [ <https://localhost/#distance> "431"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Paris"
] ;
<https://localhost/#Amsterdam> [ <https://localhost/#distance> "358"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "London"
] ;
<https://localhost/#Amsterdam> [ <https://localhost/#distance> "173"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Brussels"
] ;
<https://localhost/#Brussels> [ <https://localhost/#distance> "322"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "London"
] ;
<https://localhost/#Brussels> [ <https://localhost/#distance> "265"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Paris"
] ;
<https://localhost/#Brussels> [ <https://localhost/#distance> "173"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Amsterdam"
] ;
<https://localhost/#London> [ <https://localhost/#distance> "358"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Amsterdam"
] ;
<https://localhost/#London> [ <https://localhost/#distance> "322"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Brussels"
] ;
<https://localhost/#London> [ <https://localhost/#distance> "344"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Paris"
] ;
<https://localhost/#Paris> [ <https://localhost/#distance> "431"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Amsterdam"
] ;
<https://localhost/#Paris> [ <https://localhost/#distance> "344"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "London"
] ;
<https://localhost/#Paris> [ <https://localhost/#distance> "265"^^<http://www.w3.org/2001/XMLSchema#int> ;
<https://localhost/#to> "Brussels"
]
] ;
<https://localhost/#desc> "Distances between several cities, in kilometers." ;
<https://localhost/#updated> "2014-02-04T18:50:45" ;
<https://localhost/#uptodate> true
] .
Performance
Largest dataset tested so far: 2.95 GB / 30459482 lines of JSON to 4.5 GB / 21964039 triples in 2m10s. Hardware: x64 Windows 10 PC with Intel Core i5-7200U 2.5 GHz CPU and 16 GB RAM.
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