---
id: wiki-2026-0508-knowledge-structure
title: Knowledge Structure
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Knowledge Organization, Information Structure]
duplicate_of: none
source_trust_level: A
confidence_score: 0.85
verification_status: applied
tags: [knowledge, structure, organization, information-architecture]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: networkx, llamaindex
---

# Knowledge Structure

## 매 한 줄
> **"매 raw text → organized hierarchy"**. Knowledge Structure 는 information 을 hierarchies, taxonomies, networks, frames 로 organizing 하는 paradigm 의 study. 2026 LLM era 에서는 retrieval-augmented systems 의 indexing strategy 와 agent memory architecture 의 backbone.

## 매 핵심

### 매 Structures
- **Hierarchy / Taxonomy**: tree (subClass, partOf)
- **Network / Graph**: arbitrary relations (KG)
- **Faceted classification**: orthogonal dimensions (Ranganathan)
- **Frames / Schemata**: slots + fillers (Minsky)
- **Folksonomy**: tag-based emergent

### 매 Properties
- **Granularity**: atomic facts vs documents
- **Reasoning depth**: lookup → multi-hop → analogical
- **Update frequency**: static → real-time
- **Source provenance**: trust chain

### 매 응용
1. Wiki 의 backlinks + categories (Roam-style).
2. RAG indexing (chunks + metadata).
3. Agent long-term memory (MemGPT, LangMem).
4. Personal Knowledge Management (Zettelkasten).

## 💻 패턴

### Hierarchical taxonomy
```python
from anytree import Node, RenderTree

ai = Node("AI")
ml = Node("ML", parent=ai)
dl = Node("DL", parent=ml)
llm = Node("LLM", parent=dl)
opus = Node("Claude Opus 4.7", parent=llm)

for pre, _, node in RenderTree(ai):
    print(f"{pre}{node.name}")
```

### Faceted indexing
```python
class FacetedIndex:
    def __init__(self):
        self.facets = {}  # facet_name → {value → set(doc_ids)}
    def add(self, doc_id, facets):
        for k, v in facets.items():
            self.facets.setdefault(k, {}).setdefault(v, set()).add(doc_id)
    def query(self, **constraints):
        sets = [self.facets[k][v] for k, v in constraints.items()]
        return set.intersection(*sets) if sets else set()

idx = FacetedIndex()
idx.add("doc1", {"topic":"ML", "year":"2026", "lang":"en"})
idx.query(topic="ML", year="2026")
```

### Zettelkasten-style atomic notes
```python
import re, hashlib
def make_zettel(title, body, tags, links):
    zid = hashlib.md5(title.encode()).hexdigest()[:8]
    return {
        "id": zid, "title": title, "body": body,
        "tags": tags, "links": links,
        "wikilinks": re.findall(r"\[\[([^\]]+)\]\]", body),
    }
```

### Hierarchical RAG indexing (LlamaIndex)
```python
from llama_index.core import VectorStoreIndex, Document
from llama_index.core.node_parser import HierarchicalNodeParser

parser = HierarchicalNodeParser.from_defaults(
    chunk_sizes=[2048, 512, 128]
)
nodes = parser.get_nodes_from_documents(docs)
index = VectorStoreIndex(nodes)
```

### Frame-based knowledge
```python
restaurant_frame = {
    "name": None,
    "cuisine": None,
    "location": {"city": None, "address": None},
    "menu": [],          # list of dish frames
    "reviews": [],
}
```

## 매 결정 기준
| Use case | Structure |
|---|---|
| stable domain | Taxonomy |
| rich relations | Graph (KG) |
| multiple dims | Faceted |
| stereotyped events | Frames |
| user-generated | Folksonomy |
| LLM RAG | Hierarchical chunks + metadata |

**기본값**: Hierarchy + tags + wikilinks (hybrid).

## 🔗 Graph
- 부모: [[Knowledge-Representation]]
- 변형: [[Ontology]]
- 응용: [[Knowledge Graph]]
- Adjacent: [[Personal-Knowledge-Management]]

## 🤖 LLM 활용
**언제**: agent memory design, RAG indexing strategy, large doc collection 정리.
**언제 X**: 매 single-doc Q&A — 매 over-engineering.

## ❌ 안티패턴
- **Premature taxonomy**: domain 도 모르고 hierarchy 먼저 design → constant restructuring.
- **Single structure forced fit**: 매 problem 이 graph 인데 tree 로 강제.
- **No update mechanism**: 매 evolving knowledge 에 frozen schema.
- **Tags 없는 hierarchy**: orthogonal facet 표현 불가.

## 🧪 검증 / 중복
- Verified (Ranganathan 1933 colon classification, Minsky 1974 frames, Sowa 2000 KR textbook).
- 신뢰도 A-.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — taxonomy/graph/facet/frame structures, RAG indexing |