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Turtle Format (RDF)

Description​

Turtle is a text-based format for representing RDF (Resource Description Framework) data. It's a human-readable serialization of RDF graphs. Turtle files represent RDF triples (subject-predicate-object) in a compact, readable format.

File Extensions​

  • .ttl - Turtle RDF files
  • .turtle - Turtle RDF files (alias)

Implementation Details​

Reading​

The Turtle implementation:

  • Uses rdflib library for parsing
  • Parses RDF triples from Turtle format
  • Groups triples by subject
  • Converts each subject to a dictionary with its properties
  • Supports optional subject/predicate filtering

Writing​

Writing is not currently supported for Turtle format.

Key Features​

  • RDF format: Represents RDF data
  • Human-readable: More readable than RDF/XML
  • Triple grouping: Groups triples by subject
  • Nested data: Supports complex RDF structures
  • Filtering: Can filter by subject or predicate

Usage​

from iterable import open_iterable

# Basic reading
source = open_iterable('data.ttl')
for row in source:
    print(row)  # Each row represents a subject with its properties
source.close()

# Filter by subject
source = open_iterable('data.ttl', iterableargs={
    'subject': 'http://example.org/person1'
})

Parameters​

  • subject (str): Optional - Filter by subject URI
  • predicate (str): Optional - Filter by predicate URI
  • encoding (str): File encoding (default: utf8)

Limitations​

  1. Read-only: Turtle format does not support writing
  2. rdflib dependency: Requires rdflib package
  3. Memory usage: Entire RDF graph is loaded into memory
  4. RDF complexity: Complex RDF structures may be difficult to work with
  5. Triple grouping: Triples are grouped by subject, which may not match all use cases

Compression Support​

Turtle files can be compressed with all supported codecs:

  • GZip (.ttl.gz)
  • BZip2 (.ttl.bz2)
  • LZMA (.ttl.xz)
  • LZ4 (.ttl.lz4)
  • ZIP (.ttl.zip)
  • Brotli (.ttl.br)
  • ZStandard (.ttl.zst)

Use Cases​

  • Semantic web: Working with semantic web data
  • Linked data: Processing linked data
  • Knowledge graphs: Building and querying knowledge graphs
  • Metadata: Working with RDF metadata