58 lines
2.0 KiB
Markdown
58 lines
2.0 KiB
Markdown
---
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title: "Fuzzy Matching"
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type: concept
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tags: ["identity-resolution", "string-similarity", "normalization", "entity-matching"]
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sources: ["identity-graph-operator"]
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last_updated: 2026-04-25
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---
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# Fuzzy Matching（模糊匹配）
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## Definition
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处理"相同实体但文本表达不同"记录的能力——通过规范化（Normalization）和相似度算法，将表面不同的记录识别为同一实体。是身份解析的核心挑战之一。
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## Core Techniques
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### 1. Nickname Normalization
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```python
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nicknames = {
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"bill": "william", "bob": "robert", "jim": "james",
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"mike": "michael", "dave": "david", "joe": "joseph",
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"tom": "thomas", "dick": "richard", "jack": "john",
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}
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# "Bill Smith" → "william smith"
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```
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### 2. String Similarity
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| 算法 | 适用场景 |
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|------|----------|
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| Levenshtein Distance | 字符级编辑距离 |
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| Jaro-Winkler | 人名高权重前缀匹配 |
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| Soundex / Metaphone | 语音相似性（"Jon" = "John"） |
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| Token-based（TF-IDF） | 多词短语 |
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### 3. Field-specific Normalization
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| 字段类型 | 规范化规则 |
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|----------|------------|
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| Email | `lower().strip()` |
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| Phone | `re.sub(r"[^\d+]", "", value)` → E.164 格式 |
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| Name | Nickname expansion + lowercase |
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| Address | Street abbreviation（St→Street）、directionals（NE→Northeast） |
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## Example
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```
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记录A: "Bill Smith", wsmith@acme.com, +1-555-0142
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记录B: "William Smith", wsmith@acme.com, +15550142
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↓ Normalize + Score
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Email: 1.0（exact match）
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Name: 0.82（Bill→William nickname expansion）
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Phone: 1.0（E.164 normalized）
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────────────────────────────────
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Total: 0.94 confidence → 触发自动 merge（> 0.95 阈值接近）
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```
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## Relationship to Related Concepts
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- [[Fuzzy-Matching]] 是 [[Identity-Resolution]] scoring 层的核心技术
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- [[Blocking]] 筛选候选对后，[[Fuzzy-Matching]] 执行细粒度字段比较
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- [[Confidence-Score]] 综合所有字段的 fuzzy match scores 得出最终决策
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