Note# 📘 WikiPagerankDB

A Wikipedia PageRank Database and Neural Network toolkit in Go — designed for indexing Wikipedia text, computing link-based PageRank scores, and supporting research around semantic retrieval and ranking.

This project is a work-in-progress and includes tools to parse Wikipedia dumps, build indexes, and explore text ranking with PageRank and related techniques. :contentReference[oaicite:0]{index=0}

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🚀 What It Is

WikiPagerankDB combines multiple components and example programs to:

• Parse and index Wikipedia data
• Compute PageRank-based scores on article link structures
• Provide experimental indexing utilities (BoltDB / MySQL / in-memory)
• Present tools and samples for semantic analysis and scoring
• Explore graph- and statistics-based ranking of Wikipedia content :contentReference[oaicite:1]{index=1}

This repository is intended for experimentation, research, and building larger search/knowledge systems over Wikipedia data.

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📂 Project Contents

WikiPagerankDB/
│
├── LICENSE
├── README.md
├── index_wiki.go                # Primary indexing logic
├── sample-boltdb.go             # Example using BoltDB for persistence
├── sample-mysql.go              # Example using MySQL
├── noun-viterbi-processor.go    # NLP sample (noun extraction and processing)
├── sentenizer.go                # Sentence parsing utility
├── tools.go                     # Shared helper tools
├── tools-new(2).go              # Alternate tools implementation
└── many sample files and helpers

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🧠 How It Works

The core idea of the project:

1. Load Wikipedia XML dumps (from dumps.wikimedia.org). :contentReference[oaicite:2]{index=2}
2. Parse text sections and link structures from article data. :contentReference[oaicite:3]{index=3}
3. Build indexes and persistent storage (BoltDB, MySQL, or custom structures). :contentReference[oaicite:4]{index=4}
4. Compute PageRank scores or other link-based statistics. :contentReference[oaicite:5]{index=5}
5. Store results for querying or downstream analysis. :contentReference[oaicite:6]{index=6}

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🔧 Requirements

• Go (1.16+ recommended)
• Optional: BoltDB, MySQL, or other datastore (depending on sample usage)
• Wikipedia data dumps (XML format) from https://dumps.wikimedia.org/enwiki/latest/ :contentReference[oaicite:7]{index=7}

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📥 Getting Wikipedia Data

To download a Wikipedia article dump:

wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2

Then extract:

bzip2 -d enwiki-latest-pages-articles.xml.bz2

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▶️ Running Examples

⚡ BoltDB Example

go build sample-boltdb.go
./sample-boltdb

This will create a BoltDB-backed index and begin parsing the Wikipedia dump for indexing. :contentReference[oaicite:8]{index=8}

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☁️ MySQL Example

go build sample-mysql.go
./sample-mysql

Requires a running MySQL server and the appropriate database/schema configured. :contentReference[oaicite:9]{index=9}

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📌 Notes

• The project is experimental and in active development. :contentReference[oaicite:10]{index=10}
• Tools are designed for large text corpora — expect indexing to take time on full Wikipedia dumps. :contentReference[oaicite:11]{index=11}
• Many utilities are included for text processing, ranking, and indexing. :contentReference[oaicite:12]{index=12}

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📊 Potential Use Cases

• Building a PageRank-based search engine over Wikipedia
• Semantic knowledge indexing and retrieval
• Graph analytics on article link structures
• NLP preprocessing pipelines on large corpora

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📄 License

This project is licensed under the BSD-3-Clause license. :contentReference[oaicite:13]{index=13}

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🛠 Contributing

Contributions are welcome — including:

• Better persistent storage support
• Faster indexing and parallel processing
• Query/search interfaces
• Integration with downstream ranking models
• CLI or Web UI for exploring indexes

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📬 Acknowledgements

Inspired by large text indexing research, PageRank principles, and efforts to make Wikipedia search more semantic and link-aware. :contentReference[oaicite:14]{index=14}

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⭐ If you find this project interesting, consider starring it on GitHub! :contentReference[oaicite:15]{index=15}