Naive Mapping API

Ontology for Naive Mappings

This page documents the public, user-facing classes for direct conversion of common input formats into RDF/N-Triples. These converters follow the same high-level pattern:

converter = SomeConverter(input_file, out_file)
converter.parse(...)
converter.export()

`CSVConverter` source 

class CSVConverter(
    input_file,
    out_file,
    delimeter=",",
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)

Naively converts a CSV file into RDF triples. Each row becomes an entity, and each CSV column becomes a predicate under ontology_uri.

Parameters

Parameter	Type	Default	Description
`input_file`	`str`	required	Path to the input CSV file.
`out_file`	`str`	required	Path where the generated N-Triples file will be written.
`delimeter`	`str`	`","`	CSV delimiter used by `csv.DictReader`. The implementation uses the spelling `delimeter`.
`ontology_uri`	`str`	`"https://example.org/ontology/"`	URI prefix used to construct predicates from CSV column names.
`resource_uri`	`str`	`"https://example.org/resource/"`	URI prefix used to construct generated subject URIs.

Methods

`CSVConverter.parse(id_columns=[], type_as_key=False)` source 

parse(id_columns=[], type_as_key=False)

Parse the CSV file and populate self.triples.

Parameter	Type	Default	Description
`id_columns`	`list[str]`	`[]`	Ordered list of CSV columns to use as row identifiers. The first available value is used. If no value is found, an incremental ID is generated.
`type_as_key`	`bool`	`False`	Accepted by the public signature for consistency with other converters. It is currently not used by `CSVConverter`.

`CSVConverter.export()` source 

export()

Write the parsed triples to out_file in N-Triples-like syntax.

Example

from toposkg.converter.toposkg_lib_csv_converter import CSVConverter

converter = CSVConverter(
    input_file="data/places.csv",
    out_file="out/places.nt",
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)
converter.parse(id_columns=["id"])
converter.export()

`JSONConverter` source 

class JSONConverter(
    input_file,
    out_file,
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)

Naively converts JSON documents into RDF triples by recursively walking dictionaries and lists. Nested JSON objects can become linked resources, while literal values become typed RDF literals.

Parameters

Parameter	Type	Default	Description
`input_file`	`str`	required	Path to the input JSON file.
`out_file`	`str`	required	Path where the generated N-Triples file will be written.
`ontology_uri`	`str`	`"https://example.org/ontology/"`	URI prefix used to construct predicates and optional classes from JSON keys.
`resource_uri`	`str`	`"https://example.org/resource/"`	URI prefix used to construct generated subject URIs.

Methods

`JSONConverter.parse(id_fields=[], type_as_key=False)` source 

parse(id_fields=[], type_as_key=False)

Parse the JSON file and populate self.triples.

Parameter	Type	Default	Description
`id_fields`	`list[str]`	`[]`	Ordered list of JSON keys to use as identifiers for generated resources. If no key is found, an incremental ID is generated.
`type_as_key`	`bool`	`False`	If `True`, nested objects receive an `rdf:type` triple using the JSON key as the class name.

`JSONConverter.export()` source 

export()

Write the parsed triples to out_file.

Example

from toposkg.converter.toposkg_lib_json_converter import JSONConverter

converter = JSONConverter("data/places.json", "out/places.nt")
converter.parse(id_fields=["id"], type_as_key=True)
converter.export()

`GeoJSONConverter` source 

class GeoJSONConverter(
    input_file,
    out_file,
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)

Converts GeoJSON features into RDF triples. Feature properties are converted like JSON fields, and geometries are serialized as GeoSPARQL geo:asWKT literals connected through geo:hasGeometry.

Parameters

Parameter	Type	Default	Description
`input_file`	`str`	required	Path to the input GeoJSON file.
`out_file`	`str`	required	Path where the generated N-Triples file will be written.
`ontology_uri`	`str`	`"https://example.org/ontology/"`	URI prefix used to construct predicates from GeoJSON property names.
`resource_uri`	`str`	`"https://example.org/resource/"`	URI prefix used to construct feature and geometry URIs.

Methods

`GeoJSONConverter.parse(id_fields=[], type_as_key=False)` source 

parse(id_fields=[], type_as_key=False)

Parse all features in the GeoJSON file, create feature entities, add geometry triples, and populate self.triples.

Parameter	Type	Default	Description
`id_fields`	`list[str]`	`[]`	Ordered list of property keys to use as feature identifiers. If none is found, an incremental ID is generated.
`type_as_key`	`bool`	`False`	Accepted by the public signature. The converter stores the flag internally for nested dictionary handling.

`GeoJSONConverter.export()` source 

export()

Write the parsed feature, property, and geometry triples to out_file.

Example

from toposkg.converter.toposkg_lib_geojson_converter import GeoJSONConverter

converter = GeoJSONConverter("data/pois.geojson", "out/pois.nt")
converter.parse(id_fields=["osm_id"])
converter.export()

`XMLConverter` source 

class XMLConverter(
    input_file,
    out_file,
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)

Naively converts XML trees into RDF triples. XML elements become generated resources, attributes become predicates, and element text becomes a value literal.

Parameters

Parameter	Type	Default	Description
`input_file`	`str`	required	Path to the input XML file.
`out_file`	`str`	required	Path where the generated N-Triples file will be written.
`ontology_uri`	`str`	`"https://example.org/ontology/"`	URI prefix used to construct predicates and optional classes from XML element/attribute names.
`resource_uri`	`str`	`"https://example.org/resource/"`	URI prefix used to construct generated XML element resources.

Methods

`XMLConverter.parse(id_fields=[], type_as_key=False)` source 

parse(id_fields=[], type_as_key=False)

Parse the XML tree recursively and populate self.triples.

Parameter	Type	Default	Description
`id_fields`	`list[str]`	`[]`	Ordered list of XML attributes to use as element identifiers. If none is found, an incremental ID is generated.
`type_as_key`	`bool`	`False`	If `True`, each XML element resource receives an `rdf:type` triple using the element tag as the class name.

`XMLConverter.export()` source 

export()

Write the parsed XML triples to out_file.

Example

from toposkg.converter.toposkg_lib_xml_converter import XMLConverter

converter = XMLConverter("data/places.xml", "out/places.nt")
converter.parse(id_fields=["id"], type_as_key=True)
converter.export()

`ShapefileConverter` source 

class ShapefileConverter(
    input_file,
    out_file,
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)

Converts an ESRI Shapefile to RDF by first converting it to a temporary GeoJSON file with GeoPandas, then delegating RDF generation to GeoJSONConverter.

Parameters

Parameter	Type	Default	Description
`input_file`	`str`	required	Path to the input `.shp` file.
`out_file`	`str`	required	Path where the generated N-Triples file will be written.
`ontology_uri`	`str`	`"https://example.org/ontology/"`	URI prefix used to construct predicates from feature attributes.
`resource_uri`	`str`	`"https://example.org/resource/"`	URI prefix used to construct generated feature and geometry URIs.

Methods

`ShapefileConverter.parse(id_fields=[], type_as_key=False)` source 

parse(id_fields=[], type_as_key=False)

Read the Shapefile, convert it to temporary GeoJSON, and populate self.triples through GeoJSONConverter.

Parameter	Type	Default	Description
`id_fields`	`list[str]`	`[]`	Ordered list of attribute names to use as feature identifiers.
`type_as_key`	`bool`	`False`	Forwarded to `GeoJSONConverter.parse()`.

`ShapefileConverter.export()` source 

export()

Write the parsed triples to out_file.

`GMLConverter` source 

class GMLConverter(
    input_file,
    out_file,
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)

Converts GML to RDF by first converting it to a temporary GeoJSON file with GeoPandas, then delegating RDF generation to GeoJSONConverter.

Parameters

Parameter	Type	Default	Description
`input_file`	`str`	required	Path to the input GML file.
`out_file`	`str`	required	Path where the generated N-Triples file will be written.
`ontology_uri`	`str`	`"https://example.org/ontology/"`	URI prefix used to construct predicates from feature attributes.
`resource_uri`	`str`	`"https://example.org/resource/"`	URI prefix used to construct generated feature and geometry URIs.

Methods

`GMLConverter.parse(id_fields=[], type_as_key=False)` source 

parse(id_fields=[], type_as_key=False)

Read the GML file, convert it to temporary GeoJSON, and populate self.triples through GeoJSONConverter.

Parameter	Type	Default	Description
`id_fields`	`list[str]`	`[]`	Ordered list of feature attributes to use as identifiers.
`type_as_key`	`bool`	`False`	Forwarded to `GeoJSONConverter.parse()`.

`GMLConverter.export()` source 

export()

Write the parsed triples to out_file.

`KMLConverter` source 

class KMLConverter(
    input_file,
    out_file,
    ontology_uri="https://example.org/ontology/",
    resource_uri="https://example.org/resource/",
)

Converts KML to RDF by reading all KML layers with Fiona/GeoPandas, merging them into a temporary GeoJSON file, then delegating RDF generation to GeoJSONConverter.

Parameters

Parameter	Type	Default	Description
`input_file`	`str`	required	Path to the input KML file.
`out_file`	`str`	required	Path where the generated N-Triples file will be written.
`ontology_uri`	`str`	`"https://example.org/ontology/"`	URI prefix used to construct predicates from feature attributes.
`resource_uri`	`str`	`"https://example.org/resource/"`	URI prefix used to construct generated feature and geometry URIs.

Methods

`KMLConverter.parse(id_fields=[], type_as_key=False)` source 

parse(id_fields=[], type_as_key=False)

Read all layers in the KML file, convert them to temporary GeoJSON, and populate self.triples through GeoJSONConverter.

Parameter	Type	Default	Description
`id_fields`	`list[str]`	`[]`	Ordered list of feature attributes to use as identifiers.
`type_as_key`	`bool`	`False`	Forwarded to `GeoJSONConverter.parse()`.

`KMLConverter.export()` source 

export()

Write the parsed triples to out_file.

Example for spatial file converters

from toposkg.converter.toposkg_lib_shapefile_converter import ShapefileConverter

converter = ShapefileConverter("data/admin_units.shp", "out/admin_units.nt")
converter.parse(id_fields=["ADM_ID"])
converter.export()

Naive Mapping API

CSVConverter source

Parameters

Methods

CSVConverter.parse(id_columns=[], type_as_key=False) source

CSVConverter.export() source

Example

JSONConverter source

Parameters

Methods

JSONConverter.parse(id_fields=[], type_as_key=False) source

JSONConverter.export() source

Example

GeoJSONConverter source

Parameters

Methods

GeoJSONConverter.parse(id_fields=[], type_as_key=False) source

GeoJSONConverter.export() source

Example

XMLConverter source

Parameters

Methods

XMLConverter.parse(id_fields=[], type_as_key=False) source

XMLConverter.export() source

Example

ShapefileConverter source

Parameters

Methods

ShapefileConverter.parse(id_fields=[], type_as_key=False) source

ShapefileConverter.export() source

GMLConverter source

Parameters

Methods

GMLConverter.parse(id_fields=[], type_as_key=False) source

GMLConverter.export() source

KMLConverter source

Parameters

Methods

KMLConverter.parse(id_fields=[], type_as_key=False) source

KMLConverter.export() source

Example for spatial file converters

`CSVConverter` source 

`CSVConverter.parse(id_columns=[], type_as_key=False)` source 

`CSVConverter.export()` source 

`JSONConverter` source 

`JSONConverter.parse(id_fields=[], type_as_key=False)` source 

`JSONConverter.export()` source 

`GeoJSONConverter` source 

`GeoJSONConverter.parse(id_fields=[], type_as_key=False)` source 

`GeoJSONConverter.export()` source 

`XMLConverter` source 

`XMLConverter.parse(id_fields=[], type_as_key=False)` source 

`XMLConverter.export()` source 

`ShapefileConverter` source 

`ShapefileConverter.parse(id_fields=[], type_as_key=False)` source 

`ShapefileConverter.export()` source 

`GMLConverter` source 

`GMLConverter.parse(id_fields=[], type_as_key=False)` source 

`GMLConverter.export()` source 

`KMLConverter` source 

`KMLConverter.parse(id_fields=[], type_as_key=False)` source 

`KMLConverter.export()` source 