What is cognitive search?
Cognitive search is a search engine technology that uses artificial intelligence (AI) to quickly find relevant and accurate search results for various types of queries. Modern enterprises store vast information—like manuals, FAQs, research reports, customer service guides, and human resources documentation—across various systems. Cognitive search technologies scan large databases of disparate information and correlate data to discover answers to users' questions. For example, you can ask a question such as, "How much was spent on machinery repairs last year?” Then, cognitive search maps the question to the relevant documents and returns a specific answer.
What are the benefits of cognitive search?
Cognitive search has many benefits that make it a useful search engine technology. The following advantages are some of the main benefits of cognitive search.
Understands natural language
A cognitive search service produces more precise results without as much specification from the user. It can generate precise search results by considering many sources and crawling through structured and unstructured data. Cognitive search engine technology also understands context when it returns results. It uses natural language processing (NLP) to determine context, patterns, and meaning from human language.
Enhances productivity
Cognitive search combines information from multiple data sources and produces a comprehensive response as output. In traditional keyword searching, you have to locate all the required information across several pages. Then, you read, analyze, and summarize the information yourself.
In contrast, you can use the comprehensive search function to get answers from many documents at once. You get access to the data they need even faster. This can improve productivity and streamline data-related business processes across your organization.
Personalizes search results
Cognitive search uses machine learning (ML), which personalizes results for users over time. Instead of continually outputting the same information, it collects data and search patterns as it's used. By recording what results users are likely to click on after an initial query, it improves and produces highly relevant results much faster. Over time, it becomes smarter, more precise, and more useful.
What are some use cases of cognitive search?
Cognitive search uses natural language processing and other artificial intelligence technologies to deliver precise searching. These are some of the steps that cognitive search uses.
Data ingestion
Cognitive search first needs information to search within. It can ingest data from documents, websites, emails, internal repositories, manuals, and any other information that you want to use. It extracts information from the resources using extraction techniques like optical character recognition (OCR), entity recognition, and NLP techniques.
The extraction process aims to enable cognitive search to understand the information like a human would then catalog the content.
Data indexing
After data ingestion, cognitive search creates a searchable index of all the extracted information you have given it. Beyond just using keywords to tag data, it uses metadata, relationships between data, and supplementary information to effectively catalog all of the information.
When a user searches for something, cognitive search consults these indexes to find relevant information much faster.
User input
When a user writes a query into cognitive search, it uses NLP to decompose and comprehend what they are asking. In traditional keyword searching, a search engine recognizes keywords and produces data that aligns with that keyword.
In contrast, cognitive search attempts to understand the full context of the query, as well as personal user factors like their preferences. It combines NLP techniques like token analysis and semantic analysis to understand what the user wants to obtain from their search.
Search and retrieval
Using the user’s query, cognitive search then scans through its data indexes and finds relevant information within its repositories. It moves through the indexes, looks for the nearest neighbors, and filters out results layer by layer. Every relevant result is assigned a relevance score. The cognitive search displays the most relevant information by sorting results according to the scores.
Continuous improvement
Users can add additional filters or specifications when using a cognitive search to help produce even more specific results. Cognitive search refines its approach to queries based on previous interactions.
For example, cognitive search records what users search for and the order of their search queries. If users typically ask a certain question right after a query, then the cognitive search proactively includes information about the follow-up question, too.
Over time, cognitive search also learns more about the sentiment and meaning behind user search queries. This improves its direct responses to queries.
For example, when someone asks a specific question, cognitive search gives a specific answer. Alternatively, a more general question results in a longer response. By constantly recording interactions and learning from them, cognitive search becomes more precise and provides more relevant information over time.
How does cognitive search work?
Here are some opportunities to use cognitive search to your benefit.
Unified search experience
You can use cognitive search to create a unified search experience. As it uses natural language processing, you can get highly detailed and accurate responses by pulling information from multiple unstructured and structured data repositories. By drawing on several sources and data types, you can make data-driven decisions more easily.
For example, biotechnology company Gilead Sciences, Inc. uses AWS for cognitive search. They use the technology to organize both structured and unstructured data. The information is sourced from up to nine enterprise systems and documents from knowledge repositories.
Cognitive search has substantially reduced manual data management tasks for Gilead. The amount of time it takes to search for information by approximately 50 percent. This has fueled valuable research, experimentation, and pharmaceutical breakthroughs.
Self-service bots
You can use cognitive search solutions in self-service bots, which helps to improve on-site customer support. Users who want to know how to perform a certain task or function can type in human language and get a customized response. Cognitive search supports knowledge discovery by pulling information from various manuals, support documents, and resources.
For example, Citibot uses AWS to provide communication tools for citizens and their local and state governments. It integrates chatbot technology with cognitive search to improve the scalability and effectiveness of engagement with constituents. Citizens find the answers they seek quickly by engaging with the chatbot, which reduces wait times by up to 90 percent.
Data archive search
Many organizations have historic data stores with millions of documents, images, and transcribed text files. Cognitive search can unlock the information in the data store and summarize it for analysis and research.
For example, The Wall Street Journal uses AWS for cognitive search. Cognitive search helped accelerate the development of Talk2020. Talk2020 was an intelligent search tool that helped the publication's readers quickly search and analyze 30 years of public statements made by presidential candidates. The technology makes it easier to deeply investigate issues over time by exploring speech patterns and performing text analyses.
Employee onboarding
Cognitive search helps with any self-service tasks that employees need to carry out, like onboarding or learning new skills. It acts as a cross-reference for employees. Instead of having to contact a manager to explain how to do something, they can ask cognitive search. This usage helps to streamline self-service tasks and increase productivity.
For example, Workgrid Software uses AWS to deliver software solutions in an employee experience platform. This makes their work more connected, efficient, and productive. Through cognitive search, employees can query digital content in the organization’s database. This way, they can find answers to any questions that come up as they work.
How does generative AI improve cognitive search?
Generative artificial intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. Generative AI helps to streamline cognitive search, better understand user intent, and improve overall responses. Next, we give some examples.
Context analysis
Many generative AI tools use transformer-based machine learning models. These models have neural networks for analyzing textual data to understand its meaning.
Transformer-based AI models are useful for natural language processing and understanding. As they consider the user, data context, and user intent, they can better understand the true purpose behind a query. With improved query understanding, cognitive search can find more precise information to return.
Result summary
Generative AI can summarize larger texts into smaller segments. Cognitive search can find the most semantically relevant parts of different documents. Then, it can use generative AI to combine them and return exactly what a user wants to see. Generative AI can also maximize the quality of a result by understanding the text it returns and removing any redundancies.
Content filtering
Generative AI can filter cognitive search results by different parameters, including user authorization, query relevancy, and user preference. When generating the result text, it can ensure that it only utilizes resources the user has authority to access. Results remain relevant and useful without compromising security.
How can AWS improve your cognitive enterprise search solutions?
Amazon Web Services (AWS) offers Amazon Kendra as a cognitive search solution.
Amazon Kendra is a fully managed, highly accurate, and easy-to-use cognitive search service that’s powered by machine learning. Your developers can use it to add search capabilities to your applications. This means your end users can discover relevant information stored within the vast amount of content spread across your company.
Here are ways you can benefit from Amazon Kendra:
- Obtain a unified search experience across both structured and unstructured content repositories
- Deliver query responses using ML-powered tools
- Access fully managed answer-ranking capabilities that help to improve answer accuracy
- Fine-tune responses based on your own specific criteria, as well as other attributes like user behavior and content freshness
Get started with cognitive search on AWS by creating an account today.
