How does the full text search work?

What is full text search?

A full-text search is a method of searching for information in which an entire body of text, i.e. all the words in a document or database, are searched. Unlike a keyword search or a search for metadata, the full-text search takes into account the entire text content of the files or documents. This makes it possible to find specific information or relevant data more quickly – regardless of where it is located in the text.

A good example of applications is the internal company search for files and e-mails with searchit, the Internet search with Google, but it is also used intensively within content management systems or SQL databases.

History of full-text search: from keywords to generative AI

The history of full-text search began with simple keywording, where content was manually tagged with keywords. Later came search engines such as Altavista and Google, which worked with so-called crawlers and indexes. Today, full-text search is being revolutionized by generative AI such as ChatGPT, semantic analysis and neural networks.

Whereas in the past only exact matches were found, modern full-text search is much more intelligent: It understands the context, recognizes synonyms and searches not only for keywords, but for relevant meanings.

Types and technologies of full-text search

Today’s full-text search uses different technologies to deliver relevant search results:

1. Full-text index: This is structured data that enables content to be searched efficiently. The index is usually created automatically when data is entered.
2. SQL full-text search: Special indexes can be created in relational databases such as SQL Server to enable lightning-fast full-text queries.
3. Semantic search: This method goes beyond simply finding words. It understands the context of the information being searched for.
4. Word Embeddings & Knowledge Graphs: AI technologies such as Word2Vec or BERT recognize meanings behind words and their relationships.
5. Artificial intelligence: Machine learning enables personalized and even more precise search results.

Areas of application of the full text search

The full-text search is used in a variety of applications:

• Internet search: Google, Bing & Co. use complex full-text algorithms.
• Enterprise Search: Companies use full-text search with searchit to quickly find data and documents on the intranet or in DMS systems.
• Science & research: Large amounts of data must be quickly searchable.
• E-commerce & SEO: Online stores rely on full-text search functions to make products easier to find and increase their visibility.

Advantages and disadvantages of full text search

Advantages:

1. Precise results with correct implementation
2. Relevant information is found quickly
3. User-friendly: The user does not need to know any complex search commands
4. Automatic indexing of large amounts of data

Disadvantages:

1. High resource requirements: Indexes require a lot of memory and computing power
2. Lack of contextual understanding with simple systems
3. Possible irrelevance: Without semantic analysis, unnecessary hits may occur

Modern AI-supported full-text search can already significantly reduce many of these disadvantages and ensure better search results while remaining user-friendly.

Full-text search – an important component of modern search engines

Full-text search works by combining indexing, intelligent search algorithms and, if necessary, artificial intelligence to quickly find relevant information from large amounts of data. Whether in SQL databases, documents, on the web or in companies – full-text search is a central technology that has revolutionized searching and finding data.