Information Retrieval in Search Engines

If you are practicing SEO without understanding search systems, this post is for YOU.

Information Retrieval (IR) is the backbone of search engines, and understanding it will allow you to grasp Semantic framework effectively.  When you search for “best practices for SEO,” the IR system scans through its indexed database to find documents that contain these terms and ranks them based on relevance to your query.
IR is the science of searching for information within a large repository, such as databases, documents, or the internet, and retrieving the most relevant results in response to a user’s query.

To Understand more about you should know the main components of IR. And here are some

Document Representation:

Creating an index, similar to an index at the back of a book, but for all the documents in a database. Each word in a document is indexed along with the document’s ID.  In other words The process of converting a document into a format that can be easily searched and retrieved.

This typically involves

• Tokenizing text [Breaking down text into smaller units (tokens), typically words or terms],
• Removing stop words [Removing common words (e.g., “the”, “and”) that do not contribute significant meaning.],
• And applying stemming [ Reducing words to their base or root form (e.g., “running” to “run”)]
• Or lemmatization [Reducing words to their dictionary form (e.g., “better” to “good”)].

Inverted Index is a data structure in IR used to map terms to their locations in a set of documents. It allows quick full-text searches.

Query Representations

When a user enters a search query, the IR system processes this query to understand what the user is looking for.  Converting the user’s query into a form that can be effectively matched against the indexed documents. This often includes similar steps as document representation, such as tokenization and normalization.

Retrieval Models

The system then ranks the indexed documents based on their relevance to the query, using various algorithms and metrics (like PageRank). Retrieval models define how the IR system matches queries with documents and ranks them based on relevance.

Common retrieval models include:

Boolean Model

Simple but lacks the ability to rank documents by relevance.
Documents are retrieved if they exactly match the query terms using logical operators (AND, OR, NOT).

Vector Space Model (VSM)

Represents documents and queries as vectors in a multi-dimensional space.
Uses cosine similarity to rank documents based on their similarity to the query.

Probabilistic Model

Assumes that the probability a document is relevant to a query can be estimated.
BM25 scores documents based on term frequency, document length, and inverse document frequency.

Language Models

Treat documents and queries as samples from a probabilistic language model.
The query likelihood model computes the probability of generating the query from the document’s language model.