xgen-ontology 0.1.0

Backend-agnostic ontology / knowledge-graph toolkit — build a clean KG from documents or tables, then search it with one-shot GraphRAG. Zero infra, any graph DB.

xgen-ontology

Backend-agnostic ontology / knowledge-graph toolkit. Turn documents or tables into a clean knowledge graph — extract, resolve, dedup, induce the is-a hierarchy, govern predicates, score quality — then search it with one-shot GraphRAG. Zero infra (the whole thing runs on a pure-Python in-memory backend), zero lock-in (load into any SPARQL 1.1 store), zero hard deps in the core.

from xgen_ontology import build_from_csv

onto = build_from_csv({                       # no LLM, no DB, no API key
    "products": "product_id,name,color_id\n1,Widget,10\n2,Gadget,20",
    "colors":   "color_id,name\n10,Red\n20,Blue",
})
print(onto.stats())          # {'classes': 2, 'instances': 4, 'relations': 2, ...}
print(onto.to_turtle())      # standards RDF/Turtle
print(onto.search("what color is Widget").answer)

Build from prose with any LLM, mix tables and text freely:

from xgen_ontology import build_from_documents, CallableLLM

llm = CallableLLM(lambda p, system="": my_model(system, p))     # OpenAI / Anthropic / vLLM / …
onto = build_from_documents({
    "policy.txt": "Rule A applies to Acme Bank since 2020.",
    "colors.csv": "color_id,name\n10,Red\n20,Blue",
}, llm=llm)

Two halves of the lifecycle

Build — documents/tables → a clean graph

The pipeline is a sequence of independently-importable, backend-agnostic stages:

Stage	What it does
tabular	table → ontology with no LLM: table→Class, FK→ObjectProperty (same-name / normalized-name / value-overlap detection), column→DataProperty, dimension rows→instances; large fact/junction tables stay schema-only
extract	one LLM call per chunk batch → schema and instances, tagged to source chunks; junk (base64/degenerate) filtered first
resolve	entity resolution: fold case/whitespace/unicode + similar surface forms, guarding dates/ids and number-conflicting names
govern	predicate governance: fold surface variants of a relation, anchor to the schema vocabulary
dedup	merge synonymous classes/properties/instances — rule keys, LLM synonym groups, and embedding cosine clusters
hierarchy	keep only genuine is-a edges ("being linked is not being a subclass"), break cycles, then SCS context profiles with property inheritance
quality	a graph-reviewer score: completeness · integrity · grounding · shape
community	Louvain modularity clustering (pure Python)
emit	Turtle (zero-dep) or OWL/RDF-XML (rdflib)

Search — one-shot GraphRAG

Not an iterative ReAct loop — fire several retrieval strategies at once and fuse:

1. vector / lexical passages (what it says)
2. graph label-linking → 1-hop relations (how entities connect)
3. class enumeration — the complete "list/count" a vector index can't give
4. HippoRAG: entities of the retrieved chunks → 1-hop expansion
5. evidence assembled with MMR diversity + adaptive top-k (the decisive minority — a warning/exception — survives instead of being crowded out)
6. one LLM synthesis; honest evidence_nodes = only the nodes the answer cites

res = onto.search("which regulation applies to Acme Bank", llm=llm)
res.answer          # the synthesis
res.relations       # graph relations used
res.evidence_nodes  # nodes the answer actually cites (honest highlight)

Any graph DB, or none

The algorithms only ever talk to small protocols (GraphStore, VectorStore, LLM, GraphSink, Morphology, Embedder), never to a database:

# zero infra — pure-Python in-memory (default)
onto.search("…")

# load into any SPARQL 1.1 store (Fuseki, GraphDB, Blazegraph, Virtuoso, …)
from xgen_ontology import fuseki
store = fuseki("http://localhost:3030", "ds", user="admin", password="…")
onto.push(store, graph="urn:my-graph")           # write
onto.search("…")                                  # or search a remote store via SparqlGraph

SparqlGraph is stdlib-only (urllib) and uses portable FILTER(CONTAINS(...)), so it works on any SPARQL 1.1 endpoint — not just jena-text.

Install

pip install xgen-ontology                 # core, zero deps
pip install "xgen-ontology[rdf]"          # + rdflib (OWL / RDF-XML emit & parse)
pip install "xgen-ontology[korean]"       # + kiwipiepy (Korean morphological dedup)
pip install "xgen-ontology[vector]"       # + qdrant-client (embedding adapters)

Run the demos with no install:

python examples/build_csv.py
python examples/build_and_search.py

Design — algorithms as a library

• dependencies = [] — the core needs nothing but the standard library. The in-memory graph indexes labels with BM25 (CJK character n-grams, so Korean/CJK search works with no morphological analyzer); the Turtle writer is hand-rolled.
• English-neutral by default — no hardcoded language. Korean morphology, name→URI translation and the extraction/synthesis prompts are all pluggable; the defaults assume nothing about your domain or language.
• Bring your own everything — LLM (generate(prompt, system)), embedder, morphology, graph store. The bundled EchoLLM lets the whole pipeline run with no API key.

src/xgen_ontology/
  models.py        # Class/Property/Concepts (T-Box), Instance/Relation/DataValue (A-Box), Node/Chunk
  protocols.py     # LLM / GraphStore / VectorStore / GraphSink / Morphology / Embedder
  text.py          # tokenizer + BM25 (CJK n-grams), IRI-safe slugging
  build/
    tabular.py     # table -> ontology (no LLM)
    extract.py     # document -> ontology (LLM)
    resolve.py     # entity resolution
    govern.py      # predicate governance
    dedup.py       # rule + LLM + vector dedup
    hierarchy.py   # is-a cleaning + SCS inheritance
    quality.py     # graph-reviewer score
    community.py   # Louvain
    emit.py        # Turtle / OWL
    pipeline.py    # OntologyBuilder (wires the stages)
  backends/
    memory.py      # InMemoryGraph / InMemoryVector / InMemoryGraphSink (zero infra)
    sparql.py      # SparqlGraph — any SPARQL 1.1 store (read + write)
  search/          # fusion + one-shot GraphRAG
  ontology.py      # Ontology — the hub (search / emit / push / quality / communities)
  facade.py        # build_from_csv / build_from_documents / build_from_triples
examples/  tests/

Roadmap

• async pipeline (parallel chunk extraction + parallel search seeds)
• Neo4j / property-graph GraphStore adapter; Qdrant VectorStore adapter
• RDF-star / qualified statements (n-ary relations, provenance) in emit
• reranker / cross-encoder hook for search

License

TBD.