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koriweb d8a80f6272 chore(wiki): dangling 링크 canonical 정규화 (768파일/1200건)

이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해
끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은
과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업.
도구: Datacollect/scripts/link_reconcile_apply.mjs

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-06-08 12:24:15 +09:00

6.1 KiB

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title

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

Wikidata, DBpedia (general knowledge graph).
Gene Ontology, SNOMED CT, UMLS (biomedical).
schema.org (web markup, Google rich results).
Enterprise: data catalogs (Collibra, Atlan).
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

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