| ---
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| language: en
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| license: mit
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| tags:
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| - seo
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| - local-seo
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| - nap
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| - consistency
|
| ---
|
|
|
| # NAP-Consistency-Checker: Local SEO NAP and Entity Validation
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|
|
| **Type:** Commercial | **Domain:** SEO, Local SEO
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| **Hugging Face:** [syeedalireza/nap-consistency-checker](https://huggingface.co/syeedalireza/nap-consistency-checker)
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|
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| Validate NAP (Name, Address, Phone) consistency and entity alignment across pages and citation sources.
|
|
|
| ## Author
|
|
|
| **Alireza Aminzadeh**
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| - Hugging Face: [syeedalireza](https://huggingface.co/syeedalireza)
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| - LinkedIn: [alirezaaminzadeh](https://www.linkedin.com/in/alirezaaminzadeh)
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| - Email: alireza.aminzadeh@hotmail.com
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|
|
| ## Problem
|
|
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| Local SEO depends on consistent NAP across site and citations. Detecting variations and normalizing entities reduces confusion for users and search engines.
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|
|
| ## Approach
|
|
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| - **Input:** Text snippets or structured NAP fields (name, address, phone) from multiple sources.
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| - **Output:** Consistency flags (match/mismatch), normalized canonical form, optional entity resolution (same business or not).
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| - **Models:** Rule-based normalization (address/phone parsing) + optional transformer/NER for extracting NAP from raw text; embedding similarity for entity matching.
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|
|
| ## Tech Stack
|
|
|
| | Category | Tools |
|
| |----------|------|
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| | NLP | Hugging Face Transformers, sentence-transformers (optional) |
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| | Rules | regex, standard phone/address parsing |
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| | Data | pandas, NumPy |
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|
|
| ## Setup
|
|
|
| ```bash
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| pip install -r requirements.txt
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| ```
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|
|
| ## Usage
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|
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| ```bash
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| python inference.py --input data/citations.csv --reference data/reference_nap.csv --output output/consistency_report.csv
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| ```
|
|
|
| - `normalize.py` provides NAP normalization helpers (used by inference). No separate “build canonical” step; the reference is read from the reference CSV.
|
|
|
| ## Project structure
|
|
|
| ```
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| 09_nap-consistency-checker/
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| ├── config.py
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| ├── normalize.py # NAP normalization helpers
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| ├── inference.py # Compare citations to reference NAP
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| ├── requirements.txt
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| ├── .env.example
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| ├── data/
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| │ ├── reference_nap.csv # Canonical NAP (one row: name, address, phone)
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| │ └── citations.csv # Source, name, address, phone per citation
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| └── output/ # consistency_report.csv
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| ```
|
|
|
| ## Data
|
|
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| - **Sample data (included):** `data/reference_nap.csv` (one row: `name`, `address`, `phone`), `data/citations.csv` (columns: `source`, `name`, `address`, `phone`). Output: `name_match`, `address_match`, `phone_match`, `nap_consistent`.
|
|
|
| ## License
|
|
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| MIT.
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|
|
