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.
mvn clean install
That should produce an executable JAR file target/json2rdf-jar-with-dependencies.jar in which dependency libraries will be included.
Each version is released to the Maven central repository as com.atomgraph.etl.json/json2rdf
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
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-jar-with-dependencies.jar https://localhost/ | riot --formatted=TURTLEAlternatively, Docker execution from shell:
cat ordinary-json-document.json | docker run --rm -i -a stdin -a stdout -a stderr atomgraph/json2rdf https://localhost/ | riot --formatted=TURTLENote 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-jar-with-dependencies.jar https://localhost/ | riot --formatted=TURTLEAlternatively, Docker execution from shell:
cat city-distances.json | docker run --rm -i -a stdin -a stdout -a stderr atomgraph/json2rdf https://localhost/ | riot --formatted=TURTLETurtle 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
] .You can download your Twitter data which includes tweets in tweets.js. Remove the window.YTD.tweets.part0 = string and save the rest as tweets.json.
To get the RDF output, save the following query as tweets.rq
BASE <https://twitter.com/>
PREFIX : <#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX sioc: <http://rdfs.org/sioc/ns#>
PREFIX dct: <http://purl.org/dc/terms/>
CONSTRUCT
{
?tweet a sioc:Post ;
sioc:id ?id ;
dct:created ?created ;
sioc:content ?content ;
sioc:reply_of ?reply_of .
}
{
?tweet_obj :id ?id ;
:created_at ?created_at_string ;
:full_text ?content .
OPTIONAL
{
?tweet_obj :in_reply_to_status_id ?in_reply_to_status_id ;
:in_reply_to_screen_name ?in_reply_to_screen_name .
BIND(URI(CONCAT(?in_reply_to_screen_name, "/status/", ?in_reply_to_status_id)) AS ?reply_of)
}
BIND("atomgraphhq" AS ?username)
BIND(URI(CONCAT(?username, "/status/", ?id)) AS ?tweet)
BIND(SUBSTR(?created_at_string, 27, 4) AS ?year_string)
BIND(SUBSTR(?created_at_string, 5, 3) AS ?month_string)
BIND(SUBSTR(?created_at_string, 9, 2) AS ?day_string)
VALUES (?month_string ?month_number_string)
{
("Jan" "01")
("Feb" "02")
("Mar" "03")
("Apr" "04")
("May" "05")
("Jun" "06")
("Jul" "07")
("Aug" "08")
("Sep" "09")
("Oct" "10")
("Nov" "11")
("Dec" "12")
}
BIND(SUBSTR(?created_at_string, 12, 8) AS ?time)
BIND(SUBSTR(?created_at_string, 21, 3) AS ?tz_hours)
BIND(SUBSTR(?created_at_string, 24, 2) AS ?tz_minutes)
BIND(STRDT(CONCAT(?year_string, "-", ?month_number_string, "-", ?day_string, "T", ?time, ?tz_hours, ":", ?tz_minutes), xsd:dateTime) AS ?created)
}adjust your Twitter handle in the query string as ?username, and then run this command:
cat tweets.json | docker run --rm -i -a stdin -a stdout -a stderr atomgraph/json2rdf https://twitter.com/ > tweets.nt && \
sparql --data tweets.nt --query tweets.rq > tweets.ttlOutput sample:
<https://twitter.com/atomgraphhq/status/1535239790693699587>
a sioc:Post ;
dct:created "2022-06-10T12:37:44+00:00"^^xsd:dateTime ;
sioc:content "Follow it on GitHub!\nhttps://t.co/pu5KkOoIOX" ;
sioc:id "1535239790693699587" ;
sioc:reply_of <https://twitter.com/atomgraphhq/status/1535211486582382593> .Improvements to the mapping query are welcome.
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.