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Cognitive Search represents a new generation of enterprise search that uses artificial intelligence (AI) technology to improve users' search queries and extract relevant information from multiple different data sets. Cognitive Search capabilities go beyond the capabilities of traditional search engines to bring together numerous data sources while also providing automatic tagging and personalization. It has the potential to greatly improve the way an organization's employees discover and access information that is relevant and necessary to their work environment.

Cognitive search is different from previously available search products because it combines indexing technology with powerful artificial intelligence technologies (such as natural language processing capabilities and algorithms) to expand across a variety of data sources and types. In addition, developers can build search applications that can be embedded in business process applications, such as drug research tools and customer portals.

The main benefits organizations can gain from cognitive search include its impact on knowledge discovery – the ability of users to extract useful information from data. For example, cognitive search improves the relevance of extracted information and increases the efficiency of query responses, enabling employees to be more productive and provide better service.

Importance and benefits of cognitive search

As the variety and volume of data used within organizations continues to increase, keyword-based searches and traditional enterprise search have become inadequate. Both approaches can harm the search process and employee productivity by returning irrelevant or incomplete results that users must sort through to find the information they need.

With cognitive search, the introduction of artificial intelligence technology enables enterprise search to extract high-level meaning from content and learn from users' searches to provide increasingly relevant and complete results. Some of the overall benefits of cognitive search include:

Maximize productivity.A single search capability eliminates the need to switch between applications and eliminates time wasted on tasks such as re-entering credentials multiple times. Additionally, the unification of data tools enables organizations to streamline their business processes.
Improve employee experience and engagement.Increase employee loyalty by eliminating wasted time and improving productivity. Machine learning (ML) algorithms that provide personalized recommendations help users find relevant data faster, while the flexibility of cognitive search improves the user experience through personalization. Because employees’ search experience improves, they are more likely to continue using these tools.
Reduce operating costs.Maximizing productivity reduces an organization’s operating costs because less time and resources are required to gather information and discover knowledge. This is particularly beneficial for industries such as healthcare and legal services that process large amounts of data.

As a company grows and acquires new customers, the need to run and analyze large amounts of data increases. If a company is bringing on thousands of new customers every day, their data growth will be exponential, making it nearly impossible to keep up with the new information. Cognitive search makes it possible to decipher ever-growing data sets for use by different parts of a company.

How does cognitive search work?

Design elements used in enterprise search form the foundation for cognitive search. This means that organizations don’t need to completely rebuild their information technology (IT) departments when implementing cognitive search. AI technology then builds on this foundation to find relevant information across all available enterprise data sources.

NLP is used to understand the meaning of unstructured data in emails, documents, market research, videos, and audio recordings. Machine learning algorithms continually improve the relevance of results. Some of the most common machine learning algorithms used in cognitive search include:

ClusteringClustering: This is an unsupervised learning algorithm that groups subsets of data based on similarity. Clustering can be used when users do not want to run a search across the entire search index. The goal is to restrict the search to a specific group of documents within each cluster.
Classification: It is a supervised learning algorithm that creates a model to predict the labels of new data using a training set consisting of pre-labeled data.
return: This is another supervised learning algorithm that uses the relationship between the input and output variables to predict continuous numerical values in the data.
recommend: This usually combines various basic algorithms to produce a recommendation engine that provides potentially useful content to users. Also known as content-based recommendation, it provides personalized recommendations based on the relationship between the user's interests and the description and attributes of the document.

In addition to these ML algorithms, there is a computationally intensive process called similarity, which builds a matrix of interactions between synthetic data samples.

References

【1】https://www.techtarget.com/searchenterpriseai/definition/cognitive-search