Persistence, stores and visualisation#

atomRDF graphs are backed by RDFLib stores, so the same KnowledgeGraph API can target an in-memory store (the default), an on-disk SQL-backed store, or a high-performance Oxigraph store.

This notebook covers:

  1. Writing and re-loading a graph in Turtle / JSON-LD.

  2. Choosing a store at construction time.

  3. Visualising the graph in the notebook.

from atomrdf import KnowledgeGraph
import atomrdf.build as build

1. In-memory graph (the default)#

kg = KnowledgeGraph()
_ = build.bulk("Fe", cubic=True, graph=kg)
_ = build.bulk("Cu", cubic=True, graph=kg)
kg.n_samples

2

Write the graph to disk and round-trip it:

kg.write("demo.ttl", format="ttl")
kg2 = KnowledgeGraph(graph_file="demo.ttl")
kg2.n_samples

2

JSON-LD also works, which is often easier to consume from web tooling:

kg.write("demo.jsonld", format="json-ld")

2. Picking a different store#

atomRDF ships connectors for three stores. They are selected via the store argument:

from atomrdf import KnowledgeGraph

# Default in-process memory store:
kg = KnowledgeGraph(store="Memory")

# SQLAlchemy-backed (requires `pip install "atomrdf[sqlalchemy]"`):
kg = KnowledgeGraph(store="db", store_file="atomrdf.db")

# Oxigraph (requires `pip install "atomrdf[oxigraph]"`):
kg = KnowledgeGraph(store="Oxigraph", store_file="oxidir/")

All three stores expose the same Python API; pick the in-memory store for quick experiments and one of the persistent stores for long-running projects.

3. Visualising the graph#

kg.visualise() renders the RDF graph inline using GraphViz. hide_types=True collapses rdf:type edges so only the data relationships remain.

kg.visualise(hide_types=True, size=(40, 25))
../_images/141c989c69513fddd05fce9001a086764187d3f5a1ae18517669743a1e2afb35.svg

For very large graphs the inline view can become unwieldy; in that case export to a stand-alone HTML / SVG file and open it in a browser.