SPARQL Anything is a system for Semantic Web re-engineering that allows users to ... query anything with SPARQL.
One of the most common uses of SPARQL Anything is to start with some structured data (e.g. csv) and produce RDF using a particular ontology (e.g. gist). Here is how that can be done.
# have java and curl installed
$ curl -L -O 'https://github.com/SPARQL-Anything/sparql.anything/releases/download/0.9.0/sparql-anything-0.9.0.jar'$ cat some.csv
id,name,height_inches
5,alice,66
2,fred,67
9,william,73
# first let's write a simple query that just shows us the Facade-X representaion of the csv
$ cat some.rq
PREFIX xyz: <http://sparql.xyz/facade-x/data/>
PREFIX fx: <http://sparql.xyz/facade-x/ns/>
CONSTRUCT { ?s ?p ?o }
WHERE
{ SERVICE <x-sparql-anything:>
{ fx:properties
fx:location "some.csv" ;
fx:csv.headers "true" .
?s ?p ?o
}
}$ java -jar sparql-anything-0.9.0.jar --query some.rq
[main] INFO com.github.sparqlanything.cli.SPARQLAnything - SPARQL anything
@prefix fx: <http://sparql.xyz/facade-x/ns/> .
@prefix xyz: <http://sparql.xyz/facade-x/data/> .
[ a fx:root ;
<http://www.w3.org/1999/02/22-rdf-syntax-ns#_1>
[ xyz:height_inches "66" ;
xyz:id "5" ;
xyz:name "alice"
] ;
<http://www.w3.org/1999/02/22-rdf-syntax-ns#_2>
[ xyz:height_inches "67" ;
xyz:id "2" ;
xyz:name "fred"
] ;
<http://www.w3.org/1999/02/22-rdf-syntax-ns#_3>
[ xyz:height_inches "73" ;
xyz:id "9" ;
xyz:name "william"
]
] .# and now you can use everything you know about SPARQL to construct the RDF you want
# by transforming the Facade-X representation into the desired RDF
$ cat some.rq
PREFIX xyz: <http://sparql.xyz/facade-x/data/>
PREFIX fx: <http://sparql.xyz/facade-x/ns/>
PREFIX ex: <http://example.com/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX gist: <https://w3id.org/semanticarts/ns/ontology/gist/>
CONSTRUCT {
?person a gist:Person ;
gist:name ?name ;
gist:hasMagnitude ?height_magnitude .
?height_magnitude a gist:Extent ;
gist:hasUnitOfMeasure gist:_inch ;
gist:numericValue ?height .
}
WHERE
{ SERVICE <x-sparql-anything:>
{ fx:properties
fx:location "some.csv" ;
fx:csv.headers "true" .
?row xyz:height_inches ?height_string .
?row xyz:id ?id_string .
?row xyz:name ?name .
bind(bnode() as ?height_magnitude)
bind(xsd:float(?height_string) as ?height)
bind(iri(concat(str(ex:),"Person/",?id_string)) as ?person)
}
}$ java -jar sparql-anything-0.9.0.jar --query some.rq
@prefix ex: <http://example.com/> .
@prefix fx: <http://sparql.xyz/facade-x/ns/> .
@prefix gist: <https://w3id.org/semanticarts/ns/ontology/gist/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix xyz: <http://sparql.xyz/facade-x/data/> .
<http://example.com/Person/9>
a gist:Person ;
gist:hasMagnitude [ a gist:Extent ;
gist:hasUnitOfMeasure gist:_inch ;
gist:numericValue "73"^^xsd:float
] ;
gist:name "william" .
<http://example.com/Person/2>
a gist:Person ;
gist:hasMagnitude [ a gist:Extent ;
gist:hasUnitOfMeasure gist:_inch ;
gist:numericValue "67"^^xsd:float
] ;
gist:name "fred" .
<http://example.com/Person/5>
a gist:Person ;
gist:hasMagnitude [ a gist:Extent ;
gist:hasUnitOfMeasure gist:_inch ;
gist:numericValue "66"^^xsd:float
] ;
gist:name "alice" .- Provides a homogeneous view over heterogeneous data sources, thanks to the Facade-X meta-model (see Facade-X specification and System overview )
- Query files in plain SPARQL 1.1, via the
SERVICE <x-sparql-anything:>(see configuration) and build knowledge graphs withCONSTRUCTqueries - Supported formats: XML, JSON, CSV, HTML, Excel, Text, Binary, EXIF, File System, Zip/Tar, Markdown, YAML, Bibtex, DOCx, PPTX (see pages dedicated to single formats)
- Transforms files, inline content, or the output of an external command
- Generates RDF, RDF-Star, and tabular data (thanks to SPARQL)
- Full-fledged HTTP client to query Web APIs (headers, authentication, all methods supported)
- Functions library for RDF sequences, strings, hashes, easy entity building, ...
- Combine multiple SERVICE clauses into complex data integration queries (thanks to SPARQL)
- Query templates (using BASIL variables)
- Save and reuse SPARQL
Results Setsas input for parametric queries - Slice large CSV, JSON and XML files with an iterator-like execution style ( see #202 and #203)
- Supports an on-disk option (with Apache Jena TDB2)
SPARQL Anything uses a single generic abstraction for all data source formats called Facade-X.
Facade-X is a simplistic meta-model used by SPARQL Anything transformers to generate RDF data from diverse data sources. Intuitively, Facade-X uses a subset of RDF as a general approach to represent the source content as-it-is but in RDF. The model combines two types of elements: containers and literals. Facade-X always has a single root container. Container members are a combination of key-value pairs, where keys are either RDF properties or container membership properties. Instead, values can be either RDF literals or other containers. This is a generic example of a Facade-X data object (more examples below):
@prefix fx: <http://sparql.xyz/facade-x/ns/> .
@prefix xyz: <http://sparql.xyz/facade-x/data/> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
[] a fx:root ; rdf:_1 [
xyz:someKey "some value" ;
rdf:_1 "another value with unspecified key" ;
rdf:_2 [
rdf:type xyz:MyType ;
rdf:_1 "another value"
]
] .More details on the Facade-X metamodel can be found here.
SPARQL Anything extends the Apache Jena ARQ processors by overloading the SERVICE operator, as in the following example:
Suppose having this JSON file as input (also available at https://sparql-anything.cc/example1.json)
[
{
"name": "Friends",
"genres": [
"Comedy",
"Romance"
],
"language": "English",
"status": "Ended",
"premiered": "1994-09-22",
"summary": "Follows the personal and professional lives of six twenty to thirty-something-year-old friends living in Manhattan.",
"stars": [
"Jennifer Aniston",
"Courteney Cox",
"Lisa Kudrow",
"Matt LeBlanc",
"Matthew Perry",
"David Schwimmer"
]
},
{
"name": "Cougar Town",
"genres": [
"Comedy",
"Romance"
],
"language": "English",
"status": "Ended",
"premiered": "2009-09-23",
"summary": "Jules is a recently divorced mother who has to face the unkind realities of dating in a world obsessed with beauty and youth. As she becomes older, she starts discovering herself.",
"stars": [
"Courteney Cox",
"David Arquette",
"Bill Lawrence",
"Linda Videtti Figueiredo",
"Blake McCormick"
]
}
]With SPARQL Anything you can select the TV series starring "Courteney Cox" with the SPARQL query
PREFIX xyz: <http://sparql.xyz/facade-x/data/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX fx: <http://sparql.xyz/facade-x/ns/>
SELECT ?seriesName
WHERE {
SERVICE <x-sparql-anything:https://sparql-anything.cc/example1.json> {
?tvSeries xyz:name ?seriesName .
?tvSeries xyz:stars ?star .
?star fx:anySlot "Courteney Cox" .
}
}and get this result without caring of transforming JSON to RDF.
| seriesName |
|---|
| "Cougar Town" |
| "Friends" |
SPARQL Anything requires Java >= 17 to be installed in your operating system.
Download the latest version of the SPARQL Anything command line from
the releases page.
The command line is a file named sparql-anything-<version>.jar.
Prepare a file with the query above and name it, for example query.sparql.
The query can be executed as follows:
java -jar sparql-anything-<version>.jar -q query.sparqlSee the usage section for details on the command line interface.
SPARQL Anything is also released as a server, embedded into an instance of the Apache Jena Fuseki server.
The server requires Java >= 17 to be installed in your operating system.
Download the latest version of the SPARQL Anything server from
the releases page.
The command line is a file named sparql-anything-server-<version>.jar.
Run the server as follows:
$ java -jar sparql-anything-server-<version>.jar
[main] INFO io.github.sparqlanything.fuseki.Endpoint - sparql.anything endpoint
[main] INFO io.github.sparqlanything.fuseki.Endpoint - Starting sparql.anything endpoint..
[main] INFO io.github.sparqlanything.fuseki.Endpoint - The server will be listening on http://localhost:3000/sparql.anything
[main] INFO io.github.sparqlanything.fuseki.Endpoint - The server will be available on http://localhost:3000/sparql
[main] INFO org.eclipse.jetty.server.Server - jetty-10.0.6; built: 2021-06-29T15:28:56.259Z; git: 37e7731b4b142a882d73974ff3bec78d621bd674; jvm 11.0.10+9
[main] INFO org.eclipse.jetty.server.handler.ContextHandler - Started o.e.j.s.ServletContextHandler@782a4fff{org.apache.jena.fuseki.Servlet,/,null,AVAILABLE}
[main] INFO org.eclipse.jetty.server.AbstractConnector - Started ServerConnector@c7a975a{HTTP/1.1, (http/1.1)}{0.0.0.0:3000}
[main] INFO org.eclipse.jetty.server.Server - Started Server@35beb15e{STARTING}[10.0.6,sto=0] @889ms
[main] INFO org.apache.jena.fuseki.Server - Start Fuseki (http=3000)
Access the SPARQL UI at the address http://localhost:3000/sparql, where you can copy the query above and execute it.
See the usage section for details on the SPARQL Anything Fuseki server.
Currently, SPARQL Anything supports the following list of formats but the possibilities are limitless! The data is interpreted as in the following examples (using default settings).
A detailed description of the interpretation can be found in the following pages:
- JSON
- HTML
- CSV
- XML
- Binary
- Text
- Markdown
- File system and archives (ZIP, Tar)
- Spreadsheets: XLS, XLSx
- Documents: DOCx
- EXIF Metadata
- Bibliography
- YAML
- Slides
... and, of course, the triples generated from the these formats can be integrated with the content of any RDF Static file
SPARQL Anything behaves as a standard SPARQL query engine. For example, the SPARQL Anything server will act as a virtual endpoint that can be queried exactly as a remote SPARQL endpoint. In addition, SPARQL Anything provides a rich Command Line Interface (CLI). For information for how to run SPARQL Anything, please see the quickstart and usage sections of the documentation.
In order to instruct the query processor to delegate the execution to SPARQL Anything, you can use the following IRI-schema within SERVICE clauses. A minimal URI that uses only the resource locator is also possible. In this case SPARQL Anything guesses the data source type from the file extension.
Note: Use the file:// protocol to reference local files
Alternatively, options can be provided as basic graph pattern inside the SERVICE clause as follows
PREFIX xyz: <http://sparql.xyz/facade-x/data/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX fx: <http://sparql.xyz/facade-x/ns/>
SELECT ?seriesName
WHERE {
SERVICE <x-sparql-anything:> {
fx:properties fx:location "https://sparql-anything.cc/example1.json" .
?tvSeries xyz:name ?seriesName .
?tvSeries xyz:stars ?star .
?star fx:anySlot "Courteney Cox" .
}
}Note that
- The SERVICE IRI scheme must be
x-sparql-anything:. - Each triplification option to pass to the engine corresponds to a triple of the Basic Graph Pattern inside the SERVICE clause.
- Such triples must have
fx:propertiesas subject,fx:[OPTION-NAME]as predicate, and a literal or a variable as object.
You can also mix the two modalities as follows.
PREFIX xyz: <http://sparql.xyz/facade-x/data/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX fx: <http://sparql.xyz/facade-x/ns/>
SELECT ?seriesName
WHERE {
SERVICE <x-sparql-anything:blank-nodes=false> {
fx:properties fx:location "https://sparql-anything.cc/example1.json" .
?tvSeries xyz:name ?seriesName .
?tvSeries xyz:stars ?star .
?star fx:anySlot "Courteney Cox" .
}
}| Option name | Description | Valid Values | Default Value |
|---|---|---|---|
| location* | The URL of the data source. | Any valid URL or (absolute or relative) path of the file system. | * |
| content* | The content to be transformed. | Any valid literal. | * |
| command* | An external command line to be executed. The output is handled according to the option 'media-type' | Any valid literal. | * |
| read-from-std-in* | It tells SPARQL Anything to read the content to be transformed from standard input (see issue #244) | true/false | false |
| from-archive | The filename of the resource to be triplified within an archive. | Any filename. | No value |
| root | The IRI of generated root resource. The root will be used as a namespace for the graphs and containers that will be generated. | Any valid IRI. | location (in the case of location argument set) or 'http://sparql.xyz/facade-x/data/' + md5Hex(content) (in the case of content argument set) or 'http://sparql.xyz/facade-x/data/' + md5Hex(command) (in the case of command argument set) |
| media-type | The media-type of the data source. | Any valid Media-Type. Supported media types are specified in the pages dedicated to the supported formats | No value (the media-type will be guessed from the the file extension) |
| namespace | The namespace prefix for the properties and classes that will be generated. | Any valid namespace prefix. | http://sparql.xyz/facade-x/data/ |
| blank-nodes | It tells SPARQL Anything to generate blank nodes or not. | true/false | true |
| trim-strings | Trim all string literals. | true/false | false |
| null-string | Do not produce triples where the specified string would be in the object position of the triple. | Any string | No value |
| http.* | A set of options for customising HTTP request method, headers, querystring, and others. More details on the HTTP request configuration | No value | |
| triplifier | It forces SPARQL Anything to use a specific triplifier for transforming the data source | A canonical name of a Java class | No value |
| charset | The charset of the data source. | Any charset. | UTF-8 |
| metadata | It tells SPARQL Anything to extract metadata from the data source and to store it in the named graph with URI <http://sparql.xyz/facade-x/data/metadata> More details | true/false | false |
| ondisk | It tells SPARQL Anything to use an on disk graph (instead of the default in memory graph). The string should be a path to a directory where the on disk graph will be stored. Using an on disk graph is almost always slower (than using the default in memory graph) but with it you can triplify large files without running out of memory. | A path to a directory | No value |
| ondisk.reuse | When using an on disk graph, it tells SPARQL Anything to reuse the previous on disk graph. | true/false | true |
| strategy | The execution strategy. 0 = in memory, all triples; 1 = in memory, only triples matching any of the triple patterns in the where clause | 0,1 | 1 |
| slice | The resources is sliced and the SPARQL query executed on each one of the parts. Supported by: CSV (row by row); JSON (when array slice by item, when json object requires json.path); XML (requires xml.path) |
true/false | false |
| use-rdfs-member | It tells SPARQL Anything to use the (super)property rdfs:member instead of container membership properties (rdf:_1, rdf:_2 ...) | true/false | false |
| annotate-triples-with-slot-keys | It tells SPARQL Anything to annotate slot statements with slot keys (see issue #378) | true/false | false |
| generate-predicate-labels | It tells SPARQL Anything to create labels for extracted predicates and classes (see issue #462) | true/false | false |
| audit | It tells SPARQL Anything to generate an additional graph containing information for auditing the result of the triplification. The audit graph has the URI <http://sparql.xyz/facade-x/data/audit> | true/false | false |
| use-cache | When set to true, the result of the triplification is cached in memory and used to answer the same query again. | true/false | false |
* If read-from-std-in is set to false, it is mandatory to provide either location, content, or command.
The SPARQL Anything CLI supports parametrised queries. SPARQL Anything uses the BASIL convention for variable names in queries .
The syntax is based on the underscore character: '_', and can be easily learned by examples:
?_nameThe variable specifies the API mandatory parameter name. The value is incorporated in the query as plain literal.?__nameThe parameter name is optional.?_name_iriThe variable is substituted with the parameter value as a IRI.?_name_enThe parameter value is considered as literal with the language 'en' (e.g., en,it,es, etc.).?_name_integerThe parameter value is considered as literal and the XSD datatype 'integer' is added during substitution.?_name_prefix_datatypeThe parameter value is considered as literal and the datatype 'prefix:datatype' is added during substitution. The prefix must be specified according to the SPARQL syntax.
Variable bindings can be passed in two ways via the CLI argument -v|--values:
- Inline arguments, e.g.:
-v paramName=value1 -v paramName=value2 -v paramName2=other - Passing an SPARQL Result Set file, e.g.:
-v selectResult.xml
In the first case, the engine computes the cardinal product of all the variables bindings included and execute the query for each one of the resulting set of bindings.
In the second case, the query is executed for each set of bindings in the result set.
The following is an example of how parameter can be used in a query:
PREFIX xyz: <http://sparql.xyz/facade-x/data/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX fx: <http://sparql.xyz/facade-x/ns/>
SELECT ?seriesName
WHERE {
SERVICE <x-sparql-anything:https://sparql-anything.cc/example1.json> {
?tvSeries xyz:name ?seriesName .
?tvSeries xyz:stars ?star .
?star fx:anySlot ?_starName .
}
}The value of ?_starName can be passed via the CLI as follows:
java -jar sparql-anything-<version>.jar -q query.sparql -v starName="Courteney Cox"SPARQL Anything provides a number of magical functions and properties to facilitate the users in querying the sources and constructing knowledge graphs.
NOTE: SPARQL Anything is built on Apache Jena, see a list of supported functions on the Apache Jena documentation.
| Name | Function/Magic Property | Input | Output | Description |
|---|---|---|---|---|
| fx:anySlot | Magic Property | - | - | This property matches the RDF container membership properties (e.g. rdf:_1, rdf:_2 ...). |
| fx:cardinal(?a) | Function | Container membership property | Integer | fx:cardinal(?a) returns the corresponding cardinal integer from ?a (rdf:_24 -> 24) |
| fx:isContainerMembershipProperty(?p) | Function | Container membership property | Boolean | fx:isContainerMembershipProperty(?p) returns true if the node passed as parameter is a container membership property (rdf:_24 -> true) |
| fx:before(?a, ?b) | Function | Container membership properties | Boolean | fx:before(?a, ?b) returns true if ?a and ?b are container membership properties and ?a is lower than ?b, false otherwise |
| fx:after(?a, ?b) | Function | Container membership properties | Boolean | fx:after(?a, ?b) returns true if ?a and ?b are container membership properties and ?a is higher than ?b, false otherwise |
| fx:previous(?a) | Function | Container membership property | Container membership property | fx:previous(?a) returns the container membership property that preceeds ?a (rdf:_2 -> rdf:_1) |
| fx:next(?b) | Function | Container membership property | Container membership property | fx:next(?b) returns the container membership property that succeedes ?b (rdf:_1 -> rdf:_2) |
| fx:forward(?a, ?b) | Function | Container membership property, Integer | Container membership property | fx:forward(?a, ?b) returns the container membership property that follows ?a of ?b steps (rdf:_2, 5 -> rdf:_7) |
| fx:backward(?a, ?b) | Function | Container membership property, Integer | Container membership property | fx:backward(?a, ?b) returns the container membership property that preceeds ?a of ?b steps (rdf:_24, 4 -> rdf:_20) |
| fx:String.startsWith(?stringA, ?stringB) | Function | String, String | Boolean | fx:String.startsWith wraps java.lang.String.startsWith |
| fx:String.endsWith(?stringA, ?stringB) | Function | String, String | Boolean | fx:String.endsWith wraps java.lang.String.endsWith |
| fx:String.indexOf(?stringA, ?stringB) | Function | String, String | Integer | fx:String.indexOf wraps java.lang.String.indexOf |
| fx:String.substring(?string) | Function | String, Integer, (Integer?) | String | fx:String.substring wraps java.lang.String.substring |
| fx:String.toLowerCase(?string) | Function | String | String | fx:String.toLowerCase wraps java.lang.String.toLowerCase |
| fx:String.toUpperCase | Function | String | String | fx:String.toUpperCase wraps java.lang.String.toUpperCase |
| fx:String.replace(?string, ?characterA, ?characterB) | Function | String, Character, Character | String | fx:String.replace wraps java.lang.String.replace |
| fx:String.trim(?string) | Function | String | String | fx:String.trim wraps java.lang.String.trim |
| fx:String.stripLeading(?string) | Function | String | String | fx:String.stripLeading wraps java.lang.String.stripLeading |
| fx:String.stripTrailing(?string) | Function | String | String | fx:String.stripTrailing wraps java.lang.String.stripTrailing |
| fx:String.removeTags(?string) | Function | String | String | fx:String.removeTags removes the XML tags from the input string |
| fxWordUtils.capitalize(?string) | Function | String | String | WordUtils.capitalize wraps org.apache.commons.text.WordUtils.capitalize |
| fxWordUtils.capitalizeFully(?string) | Function | String | String | fx:WordUtils.capitalizeFully wraps org.apache.commons.text.WordUtils.capitalizeFully |
| fx:WordUtils.initials(?string) | Function | String | String | fx:WordUtils.initials wraps org.apache.commons.text.WordUtils.initials |
| fx:WordUtils.swapCase(?string) | Function | String | String | fx:WordUtils.swapCase wraps org.apache.commons.text.WordUtils.swapCase |
| fx:WordUtils.uncapitalize(?string) | Function | String | String | fx:WordUtils.uncapitalize wraps org.apache.commons.text.WordUtils.uncapitalize |
| fx:DigestUtils.md2Hex(?string) | Function | String | String | fx:DigestUtils.md2Hex wraps org.apache.commons.codec.digest.DigestUtils.md2Hex |
| fx:DigestUtils.md5Hex(?string) | Function | String | String | fx:DigestUtils.md5Hex wraps org.apache.commons.codec.digest.DigestUtils.md5Hex |
| fx:DigestUtils.sha1Hex(?string) | Function | String | String | fx:DigestUtils.sha1Hex wraps org.apache.commons.codec.digest.DigestUtils.sha1Hex |
| fx:DigestUtils.sha256Hex(?string) | Function | String | String | fx:DigestUtils.sha256Hex wraps org.apache.commons.codec.digest.DigestUtils.sha256Hex |
| fx:DigestUtils.sha384Hex(?string) | Function | String | String | fx:DigestUtils.sha384Hex wraps org.apache.commons.codec.digest.DigestUtils.sha384Hex |
| fx:DigestUtils.sha512Hex(?string) | Function | String | String | fx:DigestUtils.sha512Hex wraps org.apache.commons.codec.digest.DigestUtils.sha512Hex |
| fx:URLEncoder.encode(?string) | Function | String, String | String | fx:URLEncoder.encode wraps java.net.URLEncoder.encode |
| fx:URLDecoder.decode(?string) | Function | String, String | String | fx:URLDecoder.decode wraps java.net.URLDecoder.decode |
| fx:serial(?a ... ?n) | Function | Any sequence of nodes | Integer | The function fx:serial (?a ... ?n) generates an incremental number using the arguments as reference counters. For example, calling fx:serial("x") two times will generate 1 and then 2. Instead, calling fx:serial(?x) multiple times will generate sequential numbers for each value of ?x. |
| fx:entity(?a ... ?n) | Function | Any sequence of node | URI node | The function fx:entity (?a ... ?n) accepts a list of arguments and performs concatenation and automatic casting to string. Container membership properties (rdf:_1,rdf:_2,...) are cast to numbers and then to strings ("1","2"). |
| fx:literal(?a, ?b) | Function | String, (URI or language code) | Literal node | The function fx:literal( ?a , ?b ) builds a literal from the string representation of ?a, using ?b either as a typed literal (if a IRI is given) or a lang code (if a string of length of two is given). |
| fx:bnode(?a) | Function | Any node | Blank node | The function fx:bnode( ?a) builds a blank node enforcing the node value as local identifier. This is useful when multiple construct templates are populated with bnode generated on different query solutions but we want them to be joined in the output RDF graph. Apparently, the standard function BNODE does generate a new node for each query solution (see issue #273 for an explanatory case). |
| fx:LevenshteinDistance(?n1, ?n2) | Function | String, String | Integer | The function fx:LevenshteinDistance(?n1, ?n2) computes the Levenshtein Distance between ?n1 and ?n2 (see #182). |
| fx:CosineDistance(?n1, ?n2) | Function | String, String | Double | The function fx:CosineDistance(?n1, ?n2) computes the Cosine Distance between ?n1 and ?n2 (see #182). |
| fx:JaccardDistance(?n1, ?n2) | Function | String, String | Double | The function fx:JaccardDistance(?n1, ?n2) computes the Jaccard Distance between ?n1 and ?n2 (see #182). |
| fx:JaroWinklerDistance(?n1, ?n2) | Function | String, String | Double | The function fx:JaroWinklerDistance(?n1, ?n2) computes the Jaro-Winkler Distance between ?n1 and ?n2 (see #182). |
| fx:LongestCommonSubsequenceDistance(?n1, ?n2) | Function | Any pair of IRIs or Literals | Integer | The function fx:LongestCommonSubsequenceDistance(?n1, ?n2) computes the Longest Common Subsequence Distance between ?n1 and ?n2 (see #182). |
| fx:HammingDistance(?n1, ?n2) | Function | String, String | Integer | The function fx:HammingDistance(?n1, ?n2) computes the Hamming Distance between ?n1 and ?n2 (see #182). |
| fx:QGramDistance(?n1, ?n2) | Function | String, String | Double | The function fx:QGramDistance(?n1, ?n2) computes the QGram Distance between ?n1 and ?n2 (see #394). |
SPARQL Anything is available as Java Library, Command Line Interface, Web Application Server, and also Python library.
Note: as of version 1.0.0, SPARQL Anything requires Java 17+.
An executable JAR can be obtained from the Releases page.
The jar can be executed as follows:
usage: java -jar sparql.anything-<version> -q query [-f <output format>]
[-v <filepath | name=value> ... ] [-c option=value] [-l
filepath] [-o filepath] [-j filepath]
-q,--query <query or URL or filepath> The path or the URL to the file
storing the query to execute or
the query itself.
-o,--output <filepath> OPTIONAL - The path to the output
file. [Default: STDOUT]
-a,--append OPTIONAL - Should output to file
be appended? WARNING: this option
does not ensure that the whole
file is valid -- that is up to
the user to set up the conditions
(such as using NQ serialization
and not using blank nodes)
-e,--explain OPTIONAL - Explain query
execution
-l,--load <URL or filepath> OPTIONAL - The path or the URL to
one RDF file or a filepath to a
folder including a set of files
to be loaded. When present, the
data is loaded in memory and the
query executed against it.
-f,--format <string> OPTIONAL - Format of the output
file. Supported values: JSON,
XML, CSV, TEXT, TTL, NT, NQ.
[Default: CSV (for SELECT
queries) or TEXT (for ASK
queries) or TTL (for CONSTRUCT
queries)]
-s,--strategy <strategy> OPTIONAL - Strategy for query
evaluation. Possible values: '1'
- triple filtering (default), '0'
- triplify all data. The system
fallbacks to '0' when the
strategy is not implemented yet
for the given resource type.
-p,--output-pattern <outputPattern> OPTIONAL - Output filename
pattern, e.g.
'my-file-?friendName.json'.
Variables should start with '?'
and refer to bindings from the
input file. This option can only
be used in combination with
'input' and is ignored otherwise.
This option overrides 'output'.
-v,--values <values> OPTIONAL - Values passed as input
parameter to a query template.
When present, the query is
pre-processed by substituting
variable names with the values
provided. The argument can be
used in two ways. (1) Providing a
single SPARQL ResultSet file. In
this case, the query is executed
for each set of bindings in the
input result set. Only 1 file is
allowed. (2) Named variable
bindings: the argument value must
follow the syntax:
var_name=var_value. The argument
can be passed multiple times and
the query repeated for each set
of values.
-c,--configuration <option=value> OPTIONAL - Configuration to be
passed to the SPARQL Anything
engine (this is equivalent to
define them in the SERVICE IRI).
The argument can be passed
multiple times (one for each
option to be set).
-j,--load-jar <filepath> OPTIONAL - Filepath to an
executable JAR to be dynamically
loaded. The argument can be
passed multiple times (one for
each JAR file to be loaded).
Logging can be configured adding the following option (SLF4J).
To enable the default logger for SPARQL anything and its dependencies:
-Dorg.slf4j.simpleLogger.defaultLogLevel=trace
To enable the default logger for SPARQL anything only:
-Dorg.slf4j.simpleLogger.log.io.github.sparqlanything=trace
An executable JAR of a SPARQL-Anything-powered Fuseki endpoint can be obtained from the Releases page.
The jar can be executed as follows:
usage: java -jar sparql-anything-fuseki-<version>.jar [-p port] [-e
sparql-endpoint-path] [-g endpoint-gui-path]
-e,--path <path> The path where the server will be running on
(Default /sparql.anything).
-g,--gui <gui> The path of the SPARQL endpoint GUI (Default
/sparql).
-j,--load-jar <filepath> OPTIONAL - Filepath to an executable JAR to be
dynamically included. The argument can be
passed multiple times (one for each JAR file
to be included).
-p,--port <port> The port where the server will be running on
(Default 3000 ).
Also, a docker image can be used by following the instructions here.
SPARQL Anything is available on Maven Central. To use it as a Java library please follow the instructions here
You can extend SPARQL Anything by including new triplifiers, more details can be found here.
You can use SPARQL Anything as a Python library, see the PySPARQL-Anything project.
You can generate executable files of the command line interface and server with maven
mvn clean install -Dgenerate-cli-jar=true -Dgenerate-server-jar=true
SPARQL Anything is distributed under Apache 2.0 License
For citing SPARQL Anything in academic papers please use:
Luigi Asprino, Enrico Daga, Aldo Gangemi, and Paul Mulholland. "Knowledge Graph Construction with a façade: a unified method to access heterogeneous data sources on the Web". ACM Transactions on Internet Technology (2022) . https://doi.org/10.1145/3555312 Preprint
@article{10.1145/3555312,
author = {Asprino, Luigi and Daga, Enrico and Gangemi, Aldo and Mulholland, Paul},
title = {Knowledge Graph Construction with a Fa\c{c}ade: A Unified Method to Access Heterogeneous Data Sources on the Web},
year = {2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1533-5399},
url = {https://doi.org/10.1145/3555312},
doi = {10.1145/3555312},
abstract = {Data integration is the dominant use case for RDF Knowledge Graphs. However, Web resources come in formats with weak semantics (for example CSV and JSON), or formats specific to a given application (for example BibTex, HTML, and Markdown). To solve this problem, Knowledge Graph Construction (KGC) is gaining momentum due to its focus on supporting users in transforming data into RDF. However, using existing KGC frameworks result in complex data processing pipelines, which mix structural and semantic mappings, whose development and maintenance constitute a significant bottleneck for KG engineers. Such frameworks force users to rely on different tools, sometimes based on heterogeneous languages, for inspecting sources, designing mappings, and generating triples, thus making the process unnecessarily complicated. We argue that it is possible and desirable to equip KG engineers with the ability of interacting with Web data formats by relying on their expertise in RDF and the well-established SPARQL query language [2]. In this article, we study a unified method for data access to heterogeneous data sources with Facade-X, a meta-model implemented in a new data integration system called SPARQL Anything. We demonstrate that our approach is theoretically sound, since it allows a single meta-model, based on RDF, to represent data from (a) any file format expressible in BNF syntax, as well as (b) any relational database. We compare our method to state-of-the-art approaches in terms of usability (cognitive complexity of the mappings) and general performance. Finally, we discuss the benefits and challenges of this novel approach by engaging with the reference user community.},
journal = {ACM Transactions on Internet Technology},
keywords = {RDF, SPARQL, Meta-model, Re-engineering}
}Paul Warren, Paul Mulholland, Enrico Daga, and Luigi Asprino. "Path-based and triplification approaches to mapping data into RDF: user behaviours and recommendations". In: Semantic Web (2024), to appear Pre-print
@article{Warren2024,
title={Path-based and triplification approaches to mapping data into RDF: User behaviours and recommendations},
author={Warren, Paul and Mulholland, Paul and Daga, Enrico and Asprino, Luigi},
journal={Semantic Web},
number={(to appear)},
publisher={IOS Press}
}Enrico Daga, Luigi Asprino, Paul Mulholland, and Aldo Gangemi. "Facade-X: An Opinionated Approach to SPARQL Anything". In: Alam, Mehwish; Groth, Paul; de Boer, Victor; Pellegrini, Tassilo and Pandit, Harshvardhan J. eds. Volume 53: Further with Knowledge Graphs, Volume 53. IOS Press, pp. 58–73.
DOI: https://doi.org/10.3233/ssw210035 | PDF
@incollection{oro78973,
volume = {53},
month = {August},
author = {Enrico Daga and Luigi Asprino and Paul Mulholland and Aldo Gangemi},
booktitle = {Volume 53: Further with Knowledge Graphs},
editor = {Mehwish Alam and Paul Groth and Victor de Boer and Tassilo Pellegrini and Harshvardhan J. Pandit},
title = {Facade-X: An Opinionated Approach to SPARQL Anything},
publisher = {IOS Press},
year = {2021},
journal = {Studies on the Semantic Web},
pages = {58--73},
keywords = {SPARQL; meta-model; re-engineering},
url = {http://oro.open.ac.uk/78973/},
abstract = {The Semantic Web research community understood since its beginning how crucial it is to equip practitioners with methods to transform non-RDF resources into RDF. Proposals focus on either engineering content transformations or accessing non-RDF resources with SPARQL. Existing solutions require users to learn specific mapping languages (e.g. RML), to know how to query and manipulate a variety of source formats (e.g. XPATH, JSON-Path), or to combine multiple languages (e.g. SPARQL Generate). In this paper, we explore an alternative solution and contribute a general-purpose meta-model for converting non-RDF resources into RDF: {\ensuremath{<}}i{\ensuremath{>}}Facade-X{\ensuremath{<}}/i{\ensuremath{>}}. Our approach can be implemented by overriding the SERVICE operator and does not require to extend the SPARQL syntax. We compare our approach with the state of art methods RML and SPARQL Generate and show how our solution has lower learning demands and cognitive complexity, and it is cheaper to implement and maintain, while having comparable extensibility and efficiency.}
}
