Project Overview

This project is a basic search engine that indexes content from a set of documents and retrieves relevant search results based on user queries. It demonstrates essential search engine functions such as indexing, querying, ranking, and the importance of document relevance, which is computed using the PageRank algorithm.

Motivation/Goal

The goal of this project was to build a functional search engine to gain insight into the workings of search algorithms, especially focusing on ranking results effectively. This project emphasizes not just finding documents but ranking them by their importance, which is where the PageRank algorithm plays a crucial role.

Features

• Efficient document indexing for faster search responses.
• Basic keyword search that processes user input to find matches in documents.
• PageRank-based ranking of search results, ensuring documents with higher authority are returned first.
• Clean and user-friendly interface to interact with the search engine.

Technical Stack

Languages: Python

Frameworks/Libraries: Flask (Backend)

Database: SQLite (to store indexed documents and metadata)

APIs/Libraries: BeautifulSoup (for web scraping), NLTK (for text processing and natural language features)

Architecture/Design

The search engine system is built around a client-server architecture, consisting of two primary components:

• Indexing Server: This component processes documents, breaking them down into searchable keywords, and stores the information in an SQLite database. The index allows fast lookups for words or phrases.
• Search Server: Handles incoming search queries from the user, retrieves relevant documents from the index, ranks them using the PageRank algorithm, and returns the results to the user in order of relevance.

How PageRank Works: The PageRank algorithm is a fundamental algorithm used by Google to rank websites. PageRank works by evaluating the number and quality of links pointing to a document (or web page). A page with more high-quality backlinks is considered more important, and as such, it is ranked higher in the search results.

In the context of this search engine, each document is considered a "page," and the links are the connections between the documents. The more backlinks a document has from other high-ranking documents, the higher its PageRank score. This method of ranking results ensures that the most authoritative and relevant documents are displayed at the top.

In simple terms, if Document A links to Document B, Document B is considered more important due to the "vote" from Document A. If Document A is linked by many other documents, its importance increases, thus affecting the ranking of Document B when it's linked to it.

Challenges & Solutions

One of the major challenges was ensuring the accuracy of search results. Initially, the search was based purely on keyword matching, which led to irrelevant results. To address this, I implemented tokenization and stemming techniques using the NLTK library. These methods broke down words into their root form, improving search accuracy by matching variations of words.

The most complex part of the project was implementing the PageRank algorithm. I needed to adapt it to suit the document structure and ensure that links between documents correctly impacted the ranking. This was achieved by calculating the number of incoming and outgoing links for each document, as well as adjusting the rank based on the quality of the linking documents.

Lessons Learned

This project significantly enhanced my understanding of how large-scale search engines like Google rank pages. I learned how to apply graph theory (the concept of backlinks as links between nodes in a graph) and how a ranking algorithm like PageRank can be applied to real-world problems.

Additionally, I gained valuable experience working with databases, implementing natural language processing for query processing, and managing a full-stack architecture involving both frontend and backend components.

Future Improvements

In the future, I plan to enhance the search engine by:

• Introducing personalized search based on user preferences and browsing history.
• Adding additional search filters like date range, document type, and popularity metrics.
• Improving the PageRank algorithm by considering more features of links, such as link trustworthiness or the relevance of the linking page's content.

Conclusion

This project was an important step in understanding the core principles behind search engines. I successfully implemented document indexing, built a ranking system with PageRank, and addressed challenges in processing natural language for search queries. I’m excited to apply these concepts in more advanced projects in the future.