Cookbook

Task-oriented recipes. For tutorials, see the User guides user guide.

Formats and files

  • Serialize / parse — File I/O (parse and serialize) (TripleModel.parse, .serialize, infer_format)

  • Base URI for relative IRIs — Rdf.base_uri and parse(..., base=) in guide 10

  • Supported formats — Turtle, TriG, N-Triples, N-Quads, RDF/XML, N3, JSON-LD (pass format= explicitly when needed)

SPARQL and Fuseki

  • In-memory SPARQL — SPARQL and remote endpoints (select_models, construct_models, ask, apply_update)

  • Remote endpoints — fetch RDF into a local Store, then run CONSTRUCT/SELECT helpers (see guide 13). load_sparql / open_sparql_graph raise NotImplementedError in 0.10.0.

  • Apache Jena Fuseki (optional): export or CONSTRUCT from Fuseki, save to a file, then load locally:

from triplemodel import Store, load_graph
from triplemodel.io.sparql import construct_models

# After downloading Turtle/N-Triples from Fuseki (urllib, httpx, etc.):
graph = load_graph("fuseki-export.nt", format="nt")
people = construct_models(
    Person,
    graph,
    """
    CONSTRUCT { ?s ?p ?o }
    WHERE { ?s a foaf:Person . ?s ?p ?o }
    """,
)

Use apply_update against a writable local Store after loading data; remote SPARQL Update is out of scope for core TripleModel.

Named graphs (Dataset)

Persistent stores

  • On-disk pyoxigraph — open_graph("disk", path); call graph.close() when using a temporary directory from parse_into_store_graph; see examples/stores/disk_store.py and Stores, scale, and strict import

  • LevelDB / Kyoto / GraphDB / RDF4J — use vendor SDKs or SparqlModel directly (out of scope for core)

Scale and strict import

  • Chunked load — iter_graph_to_models, load_models_streaming (guide 15)

  • Strict mode — Rdf.strict_import, Rdf.warn_unmapped_fields

Custom plugins (literals and resolvers)

from triplemodel.plugins import register_literal_type, register_predicate_resolver

Parser/serializer/store registration was removed in 0.10.0 — see Plugin hooks and Migrating to TripleModel 0.10.0 (pyoxigraph).

Real-world datasets

Runnable examples with public vocabularies (Nobel, DCAT, Wikidata, Schema.org):

Experimental codegen

triplemodel-codegen ontology.ttl -o models.py

See Codegen (experimental).