Knowledge Hub Artificial-intelligence

Let's say we use a MySQL query like %apple%. This query will return all results that contain the word "apple" or anything similar to "apple" within the text. Here’s what MySQL might return:

• "I like apple."
• "I don't like apple."
• "I know you don't like apple."
• "Do you like Apple Watch?"

However, MySQL does not understand the meaning or context behind these sentences. It simply searches for the presence of the word "apple" without considering how the word is used in different contexts.

For example, all sentences are returned because they contain the word "apple," but MySQL cannot tell whether the sentence is positive, negative, a question, or even if it's about "Apple" the fruit or "Apple" the brand.

MySQL Query Example:

SELECT * FROM products WHERE description LIKE '%apple%';

This query retrieves all rows where the description contains the word "apple" anywhere in the text.

How NLP is Different

NLP (Natural Language Processing), on the other hand, doesn’t just look for exact words—it understands the meaning, context, and structure of the sentence. NLP can recognize grammar, the tone of the sentence (whether it's a question or statement), tense (past, present, future), and more.

Here’s how it works:

• Tokenization: NLP breaks down the query and data into smaller components, such as words or subwords.
• Vectorization: The words are then converted into numbers (called vectors), which represent their meaning in a form that computers can understand.
• Similarity Check: Once the words are converted to vectors, NLP uses mathematical methods, such as cosine similarity, to determine how close in meaning two sentences are.

For example, if the user searches for "I love apples," NLP would understand the meaning of "love" and "apples" and find sentences like "I enjoy eating fruit" or "I like apples" even if they don’t contain the exact word "apple."

Why NLP is Better for Meaningful Search

Unlike MySQL, NLP captures the meaning and context of the query, allowing the system to provide more accurate and relevant results based on intent, not just the presence of keywords. This makes NLP a much more powerful tool for natural language search where understanding what the user actually means is critical.