Inverted Index Fundamentals

Introduction

An inverted index is a data structure used to improve the speed of full-text searches on a database. It maps terms to their locations in a document or a collection of documents, enabling quick retrieval of documents that contain specific terms.

Key Concepts

• Document: Any item that contains text data.
• Term: A word or token that is indexed.
• Posting List: A list of documents (or positions within documents) that contain a specific term.
• Tokenization: The process of breaking down text into individual terms or tokens.

Inverted Index Construction

The construction of an inverted index involves several steps:

1. Document Collection: Gather all the documents you want to index.
2. Tokenization: Split the text of each document into terms.
3. Normalization: Convert terms to a standard format (e.g., lowercase, stemming).
4. Building the Index: For each term, create a posting list that contains the IDs of documents where the term appears.

Example Code Snippet

from collections import defaultdict

def build_inverted_index(documents):
    inverted_index = defaultdict(list)
    for doc_id, text in enumerate(documents):
        terms = text.lower().split()
        for term in terms:
            inverted_index[term].append(doc_id)
    return dict(inverted_index)

documents = ["Hello world", "Hello from the other side", "Goodbye world"]
index = build_inverted_index(documents)
print(index)

Best Practices

• Use stemming to reduce terms to their root form.
• Implement a mechanism to handle synonyms and related terms.
• Regularly update the index to reflect changes in the document collection.
• Consider using advanced data structures (like tries) for better performance.

FAQ