Cognitive search, unlike keyword search, crawls and ingests both structured and unstructured data. Keep in mind: experts estimate that as much as 80-90% of your data is unstructured, including email, customer surveys and social media.
Cognitive search solutions also enable developers to embed search in other applications using SDKs, APIs, and other tools. This is important because your data isn’t confined to databases: it’s scattered across the enterprise. So, search has to work where your teams work — in Slack, Salesforce, Jira, Amazon Web Services (AWS), etc.
Natural Language Processing (NLP)
Keyword search is basically a matching game played with digital data. If a user types “COVID-19” into the search bar, the results page features thousands of mentions of those exact words — some relevant, some not so much. Cognitive search, on the other hand, uses natural language processing to understand humans’ questions and predict their intent so it can deliver more relevant results.
For example, if a user types “how can I tell if I have COVID-19,” cognitive search understands this natural language query and serves up articles like, “COVID-19 Symptoms and Testing,“ “COVID-19 FAQs,” and “COVID-19 vs. flu.” “Cognitive search understands what you’re looking for even if you don’t,” explains Selvaraj. “So like a very experienced librarian, it can point you to the resources you need.”
NLP technologies are also what make cognitive search capable of responding to search queries via chatbot and voice assistant (think Siri, Alexa, etc.).
Machine Learning (ML) Algorithms
Cognitive search isn’t just more powerful than keyword search, it can also learn to be more powerful than itself. Using implicit and explicit feedback, cognitive search engines continually learn and use what they learn to fine-tune their performance, producing more relevant results over time.