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Antigravity Agent f8b21af4be Wiki cleanup: error-doc removal, dedup merge, link normalization

10_Wiki/Topics 대규모 정리:
- 오류 캡처/미완성 stub 문서 227개 제거
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- 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건
- 카테고리 MOC 6개 신규 생성
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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-20 23:52:15 +09:00

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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

title

Knowledge Graph

매 한 줄

"매 entity-relationship triples 의 graph". Knowledge Graph 는 (head, relation, tail) triple 의 collection 으로 구조화된 knowledge 를 저장하는 graph database paradigm. 2012 Google 의 도입 이후 search/RAG/agents 의 backbone 으로 자리잡았으며, 2026 LLM era 에서는 GraphRAG 와 entity-linking 으로 hallucination mitigation 에 사용.

매 핵심

매 Triple 구조

(subject, predicate, object) — RDF 표준
entity ID (e.g. wikidata Q-id) → unique reference
relation typed (employs, locatedIn, instanceOf, …)
property graph: edges 도 attributes 보유

매 Schema vs Schema-less

ontology-driven: OWL, schema.org → strict typing
LPG (labeled property graph): Neo4j flexible
emergent KG: LLM 으로 unstructured text 에서 자동 추출

매 응용

Search ranking (Google KG panels).
RAG with GraphRAG (Microsoft 2024).
Agent tool: entity disambiguation.
Recommendation (LinkedIn Economic Graph).
Drug discovery (Hetionet).

💻 패턴

NetworkX 로 KG build

import networkx as nx

G = nx.MultiDiGraph()
G.add_edge("Anthropic", "Claude", relation="created")
G.add_edge("Claude", "LLM", relation="instanceOf")
G.add_edge("Anthropic", "San Francisco", relation="locatedIn")

# query: what did Anthropic create?
for _, target, data in G.out_edges("Anthropic", data=True):
    if data["relation"] == "created":
        print(target)  # Claude

LLM 으로 triple 추출

from anthropic import Anthropic

client = Anthropic()
text = "Claude Opus 4.7 was released by Anthropic in 2026."

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=512,
    messages=[{
        "role": "user",
        "content": f"Extract (subject, predicate, object) triples as JSON from: {text}"
    }]
)
# → [["Claude Opus 4.7","releasedBy","Anthropic"], ...]

Neo4j Cypher query

// find all 2-hop neighbors of Anthropic
MATCH (a:Org {name:"Anthropic"})-[r1]->(x)-[r2]->(y)
RETURN a, r1, x, r2, y
LIMIT 100;

RDF + SPARQL

from rdflib import Graph, URIRef, Literal
g = Graph()
g.parse("dbpedia.ttl", format="turtle")
q = """
SELECT ?company WHERE {
    ?company a <http://dbpedia.org/ontology/Company> ;
             <http://dbpedia.org/property/foundedYear> "2021"^^xsd:gYear .
}
"""
for row in g.query(q):
    print(row.company)

GraphRAG retrieval

def graph_rag_query(q: str, kg, llm):
    entities = llm.extract_entities(q)
    subgraph = kg.k_hop_subgraph(entities, k=2)
    context = subgraph.to_text()
    return llm.answer(q, context=context)

Embedding-based KG completion (TransE)

import torch
import torch.nn as nn

class TransE(nn.Module):
    def __init__(self, n_ent, n_rel, dim=128):
        super().__init__()
        self.ent = nn.Embedding(n_ent, dim)
        self.rel = nn.Embedding(n_rel, dim)
    def score(self, h, r, t):
        return -torch.norm(self.ent(h) + self.rel(r) - self.ent(t), p=2, dim=-1)

매 결정 기준

상황	Approach
작은 domain, fast prototype	NetworkX in-memory
production, ACID	Neo4j
W3C standards	RDF + SPARQL
billion-scale, distributed	JanusGraph, TigerGraph
LLM RAG	GraphRAG (Microsoft)

기본값: Neo4j + LLM extraction pipeline.

🔗 Graph

부모: Knowledge Graph · Graph_Theory
변형: GraphRAG · Ontology
응용: Semantic Search · Recommendation-Systems
Adjacent: Embeddings

🤖 LLM 활용

언제: factual grounding, multi-hop reasoning, entity disambiguation 필요 시. 언제 X: pure semantic similarity 만 필요할 때 — vector DB 가 더 simple.

❌ 안티패턴

Schema explosion: 매 entity 마다 new relation 정의 → unmanageable.
Stale KG: 자동 update pipeline 없이 manual curation → 6 months 지나면 obsolete.
No entity resolution: "Anthropic" vs "anthropic Inc." vs "ANTHROPIC" → duplicate nodes.
Triple-only thinking: property graph 의 edge attribute 무시.

🧪 검증 / 중복

Verified (Bollacker 2008 Freebase, Hogan 2021 KG survey ACM CSUR).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — KG fundamentals, triples, GraphRAG, Neo4j patterns

4.8 KiB Raw Blame History