SPARQL Anything is a data integration and Semantic Web re-engineering system that implements the Façade-X meta-model, resolving the heterogeneity of sources by structurally mapping them onto a set of RDF components, upon which semantic mappings can be constructed.
PySPARQL Anything is a python wrapper for the SPARQL Anything tool. It purports to offer to Python users dealing with tasks of data integration and RDF data construction and analysis access to the capabilities offered by SPARQL Anything. Furthermore, it enables developers to inject RDF graphs into their Python RDFlib, NetworkX or pandas-powered data science processes, opening new opportunities for developing complex, data- intensive pipelines for generating and manipulating RDF data.
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"PySPARQL Anything is released on PyPI. To install it on your machine type the following in your command prompt:
$ pip install pysparql-anything
To remove PySPARQL Anything from your machine, do the following.
In your command prompt execute
$ python
>>> import pysparql_anything as sa
>>> sa.utilities.remove_sparql_anything()
>>> exit()
$ pip uninstall pysparql-anythingPySPARQL Anything comes equipped with a CLI tool. Its functionalities can therefore either be accessed through the command prompt or by importing the library into your scripts. Choosing the latter method will also enable the user to return the results of their SPARQL queries as specific Python objects.
Below we show a few basic steps to illustrate usage.
- To use the CLI tool, pen the command prompt with the current working directory set to the main folder of a SPARQL Anything project.
The CLI tool is accessed with the
sparql-anythingcommand. Executing
$ sparql-anything -h
will pull up the instructions on how to use the CLI:
Welcome to the PySPARQL Anything CLI. For the optional flags see below.
options:
-h, --help show this help message and exit
-j [JAVA ...], --java [JAVA ...]
The JVM initialisation options.
-q QUERY, --query QUERY
The path to the file storing the query to execute or the query itself.
-o OUTPUT, --output OUTPUT
The path to the output file. [Default: STDOUT]
-f FORMAT, --format FORMAT
Format of the output file. Supported values: JSON, XML, CSV, TEXT, TTL,
NT, NQ. [Default:TEXT or TTL]
-l LOAD, --load LOAD The path to one RDF file or a folder including a set of files to be
loaded. When present, the data is loaded in memory and the query executed
against it.
-v [VALUES ...], --values [VALUES ...]
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.
- To import PySPARQL Anything in one's scripts simply import the library and initialise a
pysparql_anything.sparql_anything.SparqlAnythingobject
import pysparql_anything as sa
engine = sa.SparqlAnything()Note that in both cases, if the SPARQL Anything jar isn't installed in the API's folder it will be downloaded there automatically the first time the module is imported or the CLI command has been executed.
- As an example, to execute the following query from the SPARQL Anything MusicXML showcase with PySPARQL Anything,
java -jar sparql-anything-0.8.0-SNAPSHOT.jar -q queries/populateOntology.sparql -v filePath="./musicXMLFiles/AltDeu10/AltDeu10-017.musicxml" -v fileName="AltDeu10-017" -f TTL
one does
$ sparql-anything -q queries/populateOntology.sparql -v filePath=./musicXMLFiles/AltDeu10/AltDeu10-017.musicxml fileName=AltDeu10-017 -f TTL
or
import pysparql_anything as sa
engine = sa.SparqlAnything()
engine.run(
query="queries/populateOntology.sparql",
format="ttl",
values={
"filePath" : "./musicXMLFiles/AltDeu10/AltDeu10-017.musicxml",
"fileName" : "AltDeu10-017"
}
)A live Google Colab demo demonstrating how to download, install and access the functionalities of PySPARQL Anything through its CLI and API can be found at the following link:
All of PySPARQL Anything functionalities can be accessed via the following four methods of the class
pysparql_anything.sparql_anything.SparqlAnything.
The constructor for this class is
pysparql_anything.SparqlAnything(*jvm_options: str) -> pysparql_anything.sparql_anything.SparqlAnythingwhere *jvm_options are the optional string arguments representing the user's preferred JVM options.
As an example, one may have
engine = sa.SparqlAnything("-Xrs", "-Xmx6g")NOTE: the *jvm_options are final. Once they are set they cannot be changed without starting a new process.
This limitation is unfortunately due to the nature of the interaction between the JVM and the Python environment.
Please see #6 for more information on this issue.
The keyword arguments to be passed to any of the PySPARQL Anything methods mirror the flags of SPARQL Anything CLI (See here for more info).
All of the keyword arguments except for values, requires to be assigned a dict, must be assigned a string literal.
Currently, the following arguments are supported:
query: str - The path to the file storing the query to execute or the query itself.
output: str - OPTIONAL - The path to the output file. [Default: STDOUT]
load: str - OPTIONAL - The path to one RDF file or a folder including a set of
files to be loaded. When present, the data is loaded in memory and
the query executed against it.
format: str - OPTIONAL - Format of the output file. Supported values: JSON, XML,
CSV, TEXT, TTL, NT, NQ. [Default:TEXT or TTL]
values: dict[str, str] - 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.
SparqlAnything.run(**kwargs) -> NoneMirrors the functionalities of the CLI. This can be used to run a query the output of which is to be printed on the command line or saved to a file. (See example above)
SparqlAnything.ask(**kwargs) -> boolExecutes an ASK query and returns a Python boolean True or False.
SparqlAnything.construct(graph_type: type=rdflib.graph.Graph, **kwargs) -> rdflib.graph.Graph | networkx.MultiDiGraphExecutes a CONSTRUCT query and returns a Python representation of a graph.
The graph_type argument accepts rdflib.graph.Graph (default) or networkx.MultiDiGraph.
SparqlAnything.select(output_type: type=dict, **kwargs) -> dict | pandas.DataFrameExecutes a SELECT query and returns the result as a Python object.
The output_type argument accepts dict (default) or pandas.DataFrame.
To build the source distribution and binary distribution we proceed as follows.
First, the tools we require are hatch as the build frontend (which comes with hatchling as its backend tool) and twine to upload the distribution files to PyPI.
All of these can be installed via pip install hatch or pip install twine if required.
Once the tools are ready, the build metadata can be configured in the pyproject.toml file.
NOTE: it is not needed to amend anything in this file for versioning the software, as this is set dynamically (see below for how to perform this).
After the build metadata has been defined, one can proceed to build the distribution archives.
To do this, open the command prompt on the directory containing the pyproject.toml file and run
hatch build ./dist
This command should output some text and once completed should generate a dist directory containing two files:
dist/
├── pysparql_anything-0.8.1.2-py3-none-any.whl
└── pysparql_anything-0.8.1.2.tar.gz
for example. Here the tar.gz file is a source distribution whereas the .whl file is a binary distribution.
To upload the distributions one does
twine upload dist/*
and enter the relevant PyPI credentials for this project.
NOTE: this process is not exclusive, and other frontend tools like build together with hatchling may be similarly used to generate the distributions. The only limit currently is on sticking with hatchling as the build backend.
Each version of PySPARQL Anything is tied to a released version of SPARQL Anything. Therefore, when a new version of the latter is released a new release of PySPARQL Anything should follow.
This process simply involves updating the __about__.py module of the source code. To do this, set the __version__, __SparqlAnything__ and __jarMainPath__ variables to the new values following the given structure.
As an example, to update from 0.9.0 to say 0.9.1 of SPARQL Anything, we would have
# PySPARQL ANYTHING METADATA
# PySPARQL version for the build process:
__version__ = "0.9.0.1" # --> "0.9.1.1"
# SPARQL ANYTHING METADATA
# Version of SPARQL Anything to download:
__SparqlAnything__ = "0.9.0" # --> "0.9.1"
# Path to the SPARQL Anything main class within the executable jar:
__jarMainPath__ = "io.github.sparqlanything.cli.SPARQLAnything" # Check is this path is still valid for 0.9.1
# SPARQL Anything GitHub URI:
__uri__ = "SPARQL-Anything/sparql.anything"After this build the new distribution files and upload them to PyPI.