2nd/10_Wiki/Topics/Computer_Science_and_Theory/Ontology.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-ontology	Ontology	10_Wiki/Topics	verified	self	Formal Ontology Knowledge Ontology Domain Model	none	A	0.9	applied	knowledge-representation semantic-web philosophy AI		2026-05-10	pending	language framework python/turtle rdflib/owlready2

Ontology

매 한 줄

"매 specification of a conceptualization (Gruber 1993) — 매 domain 의 entity, class, relation 의 formal definition". Aristotle 의 categories 에서 출발, Tim Berners-Lee 의 Semantic Web (RDF/OWL) 으로 web-scale 구현. 매 2026 의 사용처: knowledge graph (Wikidata, Google KG), biomedical (Gene Ontology, SNOMED CT), enterprise data fabric, LLM 의 retrieval-augmented generation grounding.

매 핵심

매 핵심 구성요소

• Class (Concept): 매 entity type (e.g., Person, Drug).
• Individual (Instance): 매 구체적 entity (e.g., :alice).
• Property: 매 entity 간 또는 entity-literal 의 binary relation.
  • ObjectProperty: 매 entity → entity (e.g., :hasParent).
  • DatatypeProperty: 매 entity → literal (e.g., :hasAge xsd:int).
• Axiom: 매 logical statement (subClassOf, equivalentClass, disjointWith).
• Hierarchy: 매 taxonomy (is-a) + partonomy (part-of).

매 stack

• RDF: 매 triple (subject, predicate, object) — graph data model.
• RDFS: 매 lightweight schema (subClassOf, domain, range).
• OWL 2: 매 description logic 기반 — 매 SROIQ(D), reasoning 가능.
• SPARQL: 매 query language (SQL for RDF).
• SHACL: 매 shape-based validation.

매 응용

1. Wikidata, DBpedia (general knowledge graph).
2. Gene Ontology, SNOMED CT, UMLS (biomedical).
3. schema.org (web markup, Google rich results).
4. Enterprise: data catalogs (Collibra, Atlan).
5. LLM grounding (GraphRAG, knowledge-graph augmented retrieval).

💻 패턴

Turtle (RDF/OWL syntax)

@prefix : <http://example.org/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

:Person a owl:Class .
:Drug a owl:Class .
:Antibiotic rdfs:subClassOf :Drug .

:hasPrescribed a owl:ObjectProperty ;
    rdfs:domain :Person ;
    rdfs:range :Drug .

:alice a :Person ;
    :hasPrescribed :amoxicillin .
:amoxicillin a :Antibiotic .

rdflib (Python) — load + query

from rdflib import Graph

g = Graph()
g.parse("ontology.ttl", format="turtle")

# SPARQL: who was prescribed an antibiotic?
q = """
PREFIX : <http://example.org/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?person ?drug WHERE {
  ?person :hasPrescribed ?drug .
  ?drug a/rdfs:subClassOf* :Antibiotic .
}
"""
for row in g.query(q):
    print(row.person, row.drug)

owlready2 (OWL with reasoning)

from owlready2 import *

onto = get_ontology("http://example.org/onto.owl")
with onto:
    class Person(Thing): pass
    class Drug(Thing): pass
    class Antibiotic(Drug): pass
    class hasPrescribed(ObjectProperty):
        domain = [Person]
        range = [Drug]

    alice = Person("alice")
    amox = Antibiotic("amoxicillin")
    alice.hasPrescribed.append(amox)

sync_reasoner_pellet()  # 매 inference: amox is Drug (subclass)

SHACL validation

:PersonShape a sh:NodeShape ;
    sh:targetClass :Person ;
    sh:property [
        sh:path :hasAge ;
        sh:datatype xsd:integer ;
        sh:minInclusive 0 ;
        sh:maxInclusive 150 ;
    ] .

LLM + ontology RAG (GraphRAG-style)

def graph_rag(question, llm, kg):
    # 1. Extract entities from question
    entities = llm.extract_entities(question)
    # 2. SPARQL: get neighborhood
    facts = []
    for e in entities:
        facts.extend(kg.query(f"""
          SELECT ?p ?o WHERE {{ <{e}> ?p ?o }} LIMIT 50
        """))
    # 3. Answer with grounded context
    return llm.generate(question, context=facts)

Ontology alignment (string + embedding)

from sentence_transformers import SentenceTransformer, util

def align_classes(onto_a_labels, onto_b_labels, threshold=0.85):
    model = SentenceTransformer("all-mpnet-base-v2")
    emb_a = model.encode(onto_a_labels, convert_to_tensor=True)
    emb_b = model.encode(onto_b_labels, convert_to_tensor=True)
    sim = util.cos_sim(emb_a, emb_b)
    matches = []
    for i, row in enumerate(sim):
        j = row.argmax().item()
        if row[j] > threshold:
            matches.append((onto_a_labels[i], onto_b_labels[j], row[j].item()))
    return matches

매 결정 기준

상황	Approach
simple tagging / faceting	flat taxonomy
domain modeling, no reasoning	RDFS
reasoning required (subsumption, equivalence)	OWL 2 + reasoner
validation rules	SHACL
massive scale, low schema	property graph (Neo4j)
LLM grounding	knowledge graph + GraphRAG

기본값: 매 enterprise → SKOS + RDFS; 매 reasoning critical → OWL 2 EL/QL profile.

🔗 Graph

• 부모: Knowledge Graph · Semantic-Web · Knowledge Representation
• 변형: OWL
• 응용: GraphRAG

🤖 LLM 활용

언제: 매 hallucination 감소를 위한 grounding, 매 enterprise data fabric, 매 named-entity resolution against canonical IDs. 언제 X: 매 small unstructured task — overhead 큼. 매 ontology engineering 비용 > 가치.

❌ 안티패턴

• OWL Full 사용: 매 reasoning undecidable. 매 OWL 2 DL profile (EL/QL/RL) 사용.
• subClassOf 의 오용 as instanceOf: 매 class hierarchy ≠ instance membership.
• No URI versioning: 매 schema 진화 시 breakage. 매 owl:versionIRI 사용.
• Free-text label only, no canonical URI: 매 alignment 불가능.
• Reasoning everything every query: 매 비싸다 — materialize 후 cache.

🧪 검증 / 중복

• Verified (Gruber 1993; W3C OWL 2 spec; Foundations of Semantic Web Technologies Hitzler et al.; GraphRAG Microsoft 2024).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Ontology FULL with RDF/OWL/SHACL/GraphRAG patterns

🛠️ 적용 사례 (Applied in summary)

🔎 코드베이스 근거 (자동 추출 — E:\Wiki 레포)

실제 구현/사용 위치:

• connectai/src/features/secondBrainTrace.ts:223 — [Omitted long matching line]

자동 생성: code_grounding.mjs · 재실행 시 갱신됨