2nd/10_Wiki/Topics/Domain_Programming/From_Other/Knowledge synthesis.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-knowledge-synthesis	Knowledge Synthesis	10_Wiki/Topics	verified	self	지식 종합 Synthesis Information Integration	none	A	0.9	applied	research knowledge synthesis methodology llm		2026-05-10	pending	language framework python rag

Knowledge Synthesis

매 한 줄

"매 synthesis = 차이의 통합". 여러 source 의 fragmented knowledge 를 단일 coherent representation 으로 통합하는 process. 2026 LLM 시대에 RAG + structured extraction + cross-document reasoning 이 표준 toolchain.

매 핵심

매 5-step pipeline

1. Acquisition: search / scrape / corpus collection.
2. Extraction: structured fact / claim 추출.
3. Alignment: same entity / concept matching across sources.
4. Integration: conflict resolution + gap filling.
5. Presentation: report / map / model.

매 synthesis types

• Aggregative: meta-analysis, systematic review.
• Interpretive: thematic synthesis, narrative review.
• Generative: 새 framework / theory 제시.

매 응용

1. Systematic literature review.
2. Competitive intelligence.
3. Wiki / knowledge graph build-out.
4. Investigation reporting.

💻 패턴

RAG synthesis pipeline

from anthropic import Anthropic
import chromadb

client = Anthropic()
db = chromadb.Client().create_collection("docs")

def synthesize(question: str, k=8):
    docs = db.query(query_texts=[question], n_results=k)["documents"][0]
    context = "\n---\n".join(docs)
    return client.messages.create(
        model="claude-opus-4-7",
        max_tokens=4096,
        system="Synthesize the sources. Cite [N]. Note conflicts.",
        messages=[{"role": "user", "content": f"Q: {question}\nSources:\n{context}"}],
    ).content[0].text

Structured extraction

from pydantic import BaseModel

class Claim(BaseModel):
    statement: str
    source: str
    confidence: float
    contradicts: list[str] = []

def extract_claims(text: str, source: str) -> list[Claim]:
    resp = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=2048,
        system="Extract atomic claims as JSON list of {statement, confidence}.",
        messages=[{"role": "user", "content": text}],
    )
    return [Claim(source=source, **c) for c in json.loads(resp.content[0].text)]

Entity alignment

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("all-MiniLM-L6-v2")

def align(entities_a, entities_b, threshold=0.85):
    emb_a = model.encode(entities_a)
    emb_b = model.encode(entities_b)
    sim = emb_a @ emb_b.T / (np.linalg.norm(emb_a, axis=1)[:, None] *
                              np.linalg.norm(emb_b, axis=1)[None, :])
    pairs = []
    for i, j in zip(*np.where(sim > threshold)):
        pairs.append((entities_a[i], entities_b[j], sim[i, j]))
    return pairs

Conflict detection

def detect_conflicts(claims: list[Claim]) -> list[tuple]:
    pairs = []
    for i, c1 in enumerate(claims):
        for c2 in claims[i+1:]:
            resp = client.messages.create(
                model="claude-haiku-4-5",
                max_tokens=10,
                messages=[{"role": "user",
                           "content": f"Contradict?\nA: {c1.statement}\nB: {c2.statement}\nYes/No"}],
            )
            if "yes" in resp.content[0].text.lower():
                pairs.append((c1, c2))
    return pairs

Hierarchical summarization

def hier_summarize(docs: list[str], chunk=4) -> str:
    if len(docs) <= 1:
        return docs[0] if docs else ""
    summaries = []
    for i in range(0, len(docs), chunk):
        merged = "\n".join(docs[i:i+chunk])
        s = spawn_summarize(merged)
        summaries.append(s)
    return hier_summarize(summaries, chunk)

Citation graph build

import networkx as nx

def build_graph(claims: list[Claim]) -> nx.DiGraph:
    g = nx.DiGraph()
    for c in claims:
        g.add_node(c.statement, source=c.source, conf=c.confidence)
        for contra in c.contradicts:
            g.add_edge(c.statement, contra, type="contradicts")
    return g

매 결정 기준

상황	Approach
수십 개 sources	RAG + hierarchical summarize
Conflicting claims	Structured extraction + LLM judge
Novel framework needed	Generative synthesis
Quantitative pooling	Meta-analysis stats

기본값: RAG + structured extraction + claim graph.

🔗 Graph

• 부모: Research Methodology · Information Retrieval
• 응용: RAG · Knowledge Graph

🤖 LLM 활용

언제: literature review, competitive analysis, contradictory source reconciliation. 언제 X: domain-novel concept invention without grounding — hallucination 위험.

❌ 안티패턴

• Cherry-picking: 매 confirming source 만 select.
• Source-blind synthesis: citation 없이 claim 통합 → traceability 상실.
• Premature consensus: conflict 무시하고 단일 narrative.
• Hallucinated alignment: LLM 이 entity 임의 동일시.

🧪 검증 / 중복

• Verified (PRISMA 2020, Cochrane Handbook 6.4, Anthropic RAG cookbook).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — 5-step pipeline + RAG/extraction 패턴