Building a Simple Search Engine - Part 2

In Part 1, we established the foundation by building a parallel web crawler using the Mercator scheme to crawl and download web pages. Now, let's delve into the second crucial step: creating an efficient inverted index for fast search.

What is an Inverted Index?

An inverted index maps words to documents where they appear, enabling efficient retrieval without scanning every document for each query. Imagine it as a detailed library map, where words are streets and documents are houses.

Building the Inverted Index:

Here's how we'll build our index in Python:

1. Preprocessing: We'll clean HTML content using beautifulsoup4 by removing irrelevant tags, stop words (using nltk.corpus.stopwords), and punctuation. Stemming or lemmatization can further normalize words (using snowball.SnowballStemmer).

2. Tokenization: We'll split the text into individual words (tokens) using nltk.word_tokenize.

3. Term Frequency: We'll count the frequency of each unique word in each document using a collections.defaultdict.

4. Posting List Creation: For each unique word, we'll create a posting list using a list or collections.deque containing the documents where it appears and their corresponding frequencies.

5. Document Indexing: We'll store additional document information like length and magnitude (calculated using TF-IDF) for later ranking.

Storing the Index:

The inverted index can be stored in text files or databases. Text files are simpler but require parsing for retrieval, while databases offer faster access but are more complex to manage. Consider using libraries like json or pickle for convenient storage and retrieval.

Challenges and Considerations:

• Scalability: As the corpus grows, the index size and search time increase. Efficient storage solutions and indexing techniques like Hashes or Tries are crucial.
• Relevance Ranking: While an inverted index helps locate documents, it doesn't tell you which are most relevant. We'll tackle this in Part 3 with ranking algorithms like Okapi BM25.
• Synonymy and Homonymy: Words with similar meanings (synonyms) or multiple meanings (homonyms) require careful handling to avoid misinterpretations. Techniques like word embeddings or thesauruses can be explored.

Stay tuned for Part 3, where we'll explore Okapi BM25 and other ranking algorithms to turn our search engine from efficient to effective!

Bonus:

• Implement a simple query interface using libraries like flask to test your search engine.
• Visualize the inverted index structure using libraries like graphviz for better understanding.
• Explore advanced indexing techniques like positional indexing or phrase searching for enhanced search capabilities.

Remember, building a search engine is an iterative process. Start with a basic version and gradually refine it as you learn more. Good luck with Part 2 and see you in Part 3!

Note: This code references specific libraries and methods for illustration. You may adapt and adjust them based on your specific needs and chosen libraries.