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
CONSTRUCTquery 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.
make build
./json2rdf.sh -i <JSON_INPUT> [-o <TTL_OUTPUT>] [-b <BASE_URL>] [-v <VOCABULARY_FILE>] [--input-charset <INPUT_CHARSET>] [--output-charset <OUTPUT_CHARSET>]
JSON_INPUT: The JSON dataTTL_OUTPUT: The resulting RDF data is written in TTL.BASE_URL: The base URI for the data. Property namespace is constructed by adding#to the base URI. Default is "https://localhost/"VOCABULARY_FILE: The vocabulary list (in URL format) to be referenced by the generated RDF file. For example,https://localhost/#Amsterdam https://localhost/#Brussels https://localhost/#cities https://localhost/#desc https://localhost/#distance https://localhost/#London https://localhost/#Paris https://localhost/#to https://localhost/#updated https://localhost/#uptodate
Additonal optional args (usually Default is enough for common needs - but you can change it as below):
--input-charset- JSON input encoding, by default UTF-8--output-charset- RDF output encoding, by default UTF-8
./json2rdf.sh -i json/city-distances.json
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test
./json2rdf.sh -i json/city-distances.json -v ontology/city-distances-vocab.txt
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charset utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charset=utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charset=utf-8 --output-charset=utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --output-charset=utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --output-charset utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --output-charset=utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charset=utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charsetutf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charset utf-8
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charset=utf-8 --output-charset=utf-16
./json2rdf.sh -i json/city-distances.json -b https://127.0.0.1/test --input-charset=utf-8 --output-charset=utf-8
Bob DuCharme's blog post on using JSON2RDF: Converting JSON to RDF.
JSON data in example.json
{
"name": "Markus Lanthaler",
"homepage": "http://www.markus-lanthaler.com/",
"image": "http://twitter.com/account/profile_image/markuslanthaler"
}Docker execution from shell:
./json2rdf.sh -i example.json
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}
]
}
}Docker execution from shell:
./json2rdf.sh -i json/city-distances.json
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
] .(from upstream's Performance text - not by me since this section is just inheriting from upstream.) 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.