OntoEnv Python Bindings

Installation

pip install ontoenv

Usage

from ontoenv import OntoEnv
from rdflib import Graph

# creates a new environment in the current directory, or loads
# an existing one. To use a different directory, pass the 'path'
# argument: OntoEnv(path="/path/to/env")
# OntoEnv() will discover ontologies in the current directory and
# its subdirectories
env = OntoEnv()

# add an ontology from a file path.
# env.add returns the name of the ontology, which is its URI
# e.g. "https://brickschema.org/schema/1.4-rc1/Brick"
brick_name = env.add("../brick/Brick.ttl")
print(f"Added ontology {brick_name}")

# When you add from a URL whose declared ontology name differs (for example a
# versioned IRI served at a versionless URL), ontoenv records that alias. You
# can later refer to the ontology by either the canonical name or the original
# URL when resolving imports or querying.

# get the graph of the ontology we just added
# env.get_graph returns an rdflib.Graph
brick_graph = env.get_graph(brick_name)
print(f"Brick graph has {len(brick_graph)} triples")

# get the full closure of the ontology, including all of its imports
# returns a tuple (rdflib.Graph, list[str])
brick_closure_graph, _ = env.get_closure(brick_name)
print(f"Brick closure has {len(brick_closure_graph)} triples")

# you can also add ontologies from a URL
rec_name = env.add("https://w3id.org/rec/rec.ttl")
rec_graph = env.get_graph(rec_name)
print(f"REC graph has {len(rec_graph)} triples")

# you can add an in-memory rdflib.Graph directly
in_memory = Graph()
in_memory.parse(data="""
@prefix owl: <http://www.w3.org/2002/07/owl#> .
<http://example.com/in-memory> a owl:Ontology .
""", format="turtle")
in_memory_name = env.add(in_memory)
print(f"Added in-memory ontology {in_memory_name}")

# if you have an rdflib.Graph with an owl:Ontology declaration,
# you can transitively import its dependencies into the graph
g = Graph()
# this graph just has one triple: the ontology declaration for Brick
g.parse(data="""
@prefix owl: <http://www.w3.org/2002/07/owl#> .
<https://brickschema.org/schema/1.4-rc1/Brick> a owl:Ontology .
""")
# this will load all of the owl:imports of the Brick ontology into 'g'
env.import_dependencies(g)
print(f"Graph with imported dependencies has {len(g)} triples")

Namespace prefixes

OntoEnv can extract namespace prefix mappings from ontology source files. Prefixes come from both parser-level declarations (@prefix in Turtle, PREFIX in SPARQL-style syntaxes) and SHACL sh:declare entries.

# Get all namespaces across the entire environment
all_ns = env.get_namespaces()
# {'owl': 'http://www.w3.org/2002/07/owl#', 'brick': 'https://brickschema.org/schema/Brick#', ...}

# Get namespaces for a single ontology
ns = env.get_namespaces("https://brickschema.org/schema/1.4-rc1/Brick")

# Include namespaces from transitive owl:imports
ns_with_imports = env.get_namespaces("https://brickschema.org/schema/1.4-rc1/Brick", include_closure=True)

From the CLI:

ontoenv namespaces                                     # all namespaces
ontoenv namespaces https://example.org/my-ontology     # single ontology
ontoenv namespaces https://example.org/my-ontology --closure   # with imports
ontoenv namespaces --json                              # JSON output

Custom graph store

If you want OntoEnv to write graphs into an existing Python-backed store, pass a graph_store object that implements a small protocol:

class GraphStore:
    def add_graph(self, iri: str, graph: Graph, overwrite: bool = False) -> None: ...
    def get_graph(self, iri: str) -> Graph: ...
    def remove_graph(self, iri: str) -> None: ...
    def graph_ids(self) -> list[str]: ...
    def size(self) -> dict[str, int]: ...  # optional, returns {"num_graphs": ..., "num_triples": ...}

Example:

store = DictGraphStore()
env = OntoEnv(graph_store=store, temporary=True)

graph_store is currently incompatible with recreate and create_or_use_cached.

RDFLib store with Rust SPARQL

If you want to use ontoenv as an rdflib store directly, use OntoEnvStore. This gives you normal rdflib.Graph and rdflib.Dataset objects, but executes SPARQL through the Rust backend instead of rdflib's Python query engine.

Use env.snapshot_as_dataset() to get a read-only rdflib.Dataset view of the env. The default backend="auto" picks the zero-copy rdf5d snapshot when a persistent .ontoenv/store.r5tu exists and otherwise falls back to an in-memory copy. Pass backend="rdf5d" to require the fast path (raises for temporary or graph_store= envs) or backend="copy" to always materialize.

from rdflib import URIRef
from ontoenv import OntoEnv

env = OntoEnv(path=".demo-env", recreate=True, offline=True, search_directories=["./brick"])
brick_name = env.add("./brick/Brick.ttl")
env.update()
env.flush()

dataset = env.snapshot_as_dataset()

for row in dataset.query(
    """
    SELECT ?entity ?label
    WHERE {
      GRAPH <https://brickschema.org/schema/1.4/Brick> {
        ?entity <http://www.w3.org/2000/01/rdf-schema#label> ?label .
      }
    }
    LIMIT 5
    """
):
    print(row.entity, row.label)

brick_graph = dataset.graph(URIRef(brick_name))
print(len(brick_graph))
env.close()

Importing ontoenv also registers the rdflib plugin name "ontoenv", so this works too:

from rdflib import Graph
import ontoenv

graph = Graph(store="ontoenv")

See demo_rdflib_store.py for a complete runnable example.

CLI Entrypoint

Installing ontoenv also provides the Rust-backed ontoenv command-line tool:

pip install ontoenv
ontoenv --help

The CLI is identical to the standalone ontoenv-cli binary; see the top-level README for usage.