What is cognitive search?
Posted: Sun Jan 19, 2025 9:48 am
Cognitive search is an information retrieval method that uses sophisticated artificial intelligence (AI) technologies such as machine learning (ML) algorithms, deep learning, and natural language processing (NLP) to process, organize, and retrieve information.
These AI-powered search engines self-learn and refine themselves as they process new data to provide users with intuitive, context-aware results.
Here's how it differs from typical corporate search systems:
How it searches: Traditional information retrieval compares the words in the query to those in documents, often without considering the context. However, cognitive search understands the meaning of the query and delivers more accurate and relevant results.
How it improves: Traditional search systems don't adapt to user behavior. Cognitive search, on the other hand, learns from the answers to your queries, refining its results over time to become more useful.
Where it searches: Traditional enterprise search typically pulls data from a single structured source, such as a database. Cognitive search, on the other hand, draws on multiple sources, including both structured and unstructured data, to give you broader search capabilities.
How it handles data: As data grows, traditional enterprise search systems can audit directors auditors email list slow down or struggle. Cognitive search is designed to handle large amounts of complex data while maintaining fast and accurate results.
Now, let's see how traditional search and cognitive search compare at a higher granularity
Criteria, traditional search, cognitive search
Retrieval mechanism: It relies heavily on keyword matching. It uses ML, NLP, and deep learning algorithms to understand the query and extract contextual information
Data sources: single source, structured data, multiple sources, unstructured data
Criteria Traditional search Cognitive search
User Introduction Request a full consultation with specific keywords Support for natural language queries
Improvement Static, only provides basic information Dynamic, learn user relationships and history to generate valuable and meaningful insights
Improvement: Static, only provides basic information; Dynamic, learns user relationships and history to generate valuable and meaningful information
Scalability: Problems with large data sources. Handles large amounts of data
Traditional search vs. cognitive search
These AI-powered search engines self-learn and refine themselves as they process new data to provide users with intuitive, context-aware results.
Here's how it differs from typical corporate search systems:
How it searches: Traditional information retrieval compares the words in the query to those in documents, often without considering the context. However, cognitive search understands the meaning of the query and delivers more accurate and relevant results.How it improves: Traditional search systems don't adapt to user behavior. Cognitive search, on the other hand, learns from the answers to your queries, refining its results over time to become more useful.
Where it searches: Traditional enterprise search typically pulls data from a single structured source, such as a database. Cognitive search, on the other hand, draws on multiple sources, including both structured and unstructured data, to give you broader search capabilities.
How it handles data: As data grows, traditional enterprise search systems can audit directors auditors email list slow down or struggle. Cognitive search is designed to handle large amounts of complex data while maintaining fast and accurate results.
Now, let's see how traditional search and cognitive search compare at a higher granularity
Criteria, traditional search, cognitive search
Retrieval mechanism: It relies heavily on keyword matching. It uses ML, NLP, and deep learning algorithms to understand the query and extract contextual information
Data sources: single source, structured data, multiple sources, unstructured data
Criteria Traditional search Cognitive search
User Introduction Request a full consultation with specific keywords Support for natural language queries
Improvement Static, only provides basic information Dynamic, learn user relationships and history to generate valuable and meaningful insights
Improvement: Static, only provides basic information; Dynamic, learns user relationships and history to generate valuable and meaningful information
Scalability: Problems with large data sources. Handles large amounts of data
Traditional search vs. cognitive search