Unveiling the Secrets Behind Google: How Information Retrieval Works

Introduction
Since the internet has become an integral part of our daily lives, the search for information has become an increasingly common activity. And it is in this scenario that Google, the internet’s main search engine, plays a crucial role in the fast and efficient retrieval of information. However, how does Google manage to find relevant information in millions of web pages in a matter of seconds? The answer lies in its information retrieval system, which uses advanced algorithms to index, classify, and present the most relevant results to users. In this text, I will explore the different stages involved in Google’s information retrieval process, from document collection to result classification.
What is Google?
Google is a company, but it can also be considered an information retrieval system. These systems aim to store, organize, and create document structures that allow for quick retrieval of information through searches written in natural language.
Difference between databases and information retrieval systems
One of the main differences between database systems and information retrieval systems is how data is stored and retrieved. While databases work with structured data, such as tables and fields, information retrieval systems deal with unstructured data, such as texts and social media posts.
Generic architecture of an information retrieval system
The process of document collection is the first step in the architecture of an information retrieval system. In the case of Google, this collection is done through web scraping, which involves browsing websites and saving the content of these pages in a local repository.
After collection, the documents undergo an indexing process, where they are organized and processed to generate an inverted index. This index is used to perform queries and retrieve relevant documents for the user.
When a user performs a search, the information retrieval system looks in the inverted index for documents that match that search and then ranks the results according to their relevance to the user.
Text preprocessing
Text preprocessing involves several steps to convert written text in natural language into a format that can be easily analyzed and indexed. These steps include:
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Normalization: Transforming all text into lowercase to ensure uniformity.
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Tokenization: Converting text into a vector of words, dividing it into smaller units, called tokens.
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Stop Word Removal: Removing words with low semantic quality, such as prepositions and articles, to reduce data dimensionality.
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Lemmatization: Simplifying words by removing affixes and reducing them to their root form, to further reduce the word dictionary.
Statistical metrics
After preprocessing, some statistical metrics are used to determine the importance of words in documents and the corpus as a whole. Some of them are:
- TF (Term Frequency): How often a specific word appears in a document relative to the total number of words in the document.
- IDF (Inverse Document Frequency): A measure to identify rare or unique words in a set of documents.
- TF-IDF (Term Frequency-Inverse Document Frequency): A combination of TF and IDF, multiplying the TF value of a word in a document by the IDF value of that word in the corpus. This helps identify important and relevant words for a specific document in relation to the corpus.
Search stage
The search process in information retrieval systems is quite simple: the user enters a query in natural language, which goes through several preprocessing steps, such as normalization, tokenization, and removal of low-quality semantic words. The query is then compared to an inverted index, which links words and the documents in which they appear. Based on this comparison, relevant documents are returned and ranked according to their relevance to the user’s query.
There are several ranking measures that can be used to determine a document’s relevance, with cosine similarity and BM25 being some of the most common. Cosine similarity is a geometric measure that considers the distance between word vectors, while BM25 is a probabilistic measure that estimates the likelihood of a query being relevant to a specific document.
With advances in artificial intelligence and natural language processing, information retrieval systems can also use semantic approaches, which take into account the meaning of words and not just their frequency. This allows for more relevant and accurate results for users.
How to build an information retrieval system
If you want to build your own information retrieval system, there are tools and libraries available that make this process easier. One option is to use the Apache Lucene library, which is open-source and allows for creating custom search systems. Another alternative