Cognitive Search: What It Is And How Data Is Used In Cognitive Search

Cognitive search associates artificial intelligence skills to the indexer pipeline: how it works, what benefits and applications

What is Cognitive Search?

Cognitive search, or cognitive search, is a particular type of data search for information that uses machine learning and artificial intelligence skills to increase the speed of the process and its relevance.

The cognitive search goes beyond the standard capabilities of search engines because it integrates multiple data sources, even heterogeneous and unstructured, and can understand user intent, patterns, and relationships existing in different datasets.

Cognitive search crosses indexing techniques and artificial intelligence skills such as natural language processing and image processing to access different data in different formats (text, video, images, audio), analyze them through automatic learning, finding correlations in the different datasets, reconstructing the context from which to extract the data and responding to the search in an increasingly refined way as the database grows.

The relevance of the answer in the search is given by analyzing the different meanings that a word can assume and the context in which it is inserted: the cognitive search allows to enhance the contextual processing and, therefore, the effectiveness of the response to the query.

How Cognitive research works and how the data is used

Cognitive research derives contextual insights from conceptual data: that is, from data already processed and correlated by artificial intelligence (abstract information), it can deduce the most appropriate contextual elements to answer the user’s question.

In search engines, software that analyzes, collects, and indexes the contents of a database (or network) in an automated way are called crawlers. The crawlers analyze the different data available, explore new ones and decide whether to add them (and how) to the already created search index.

Each crawler, or indexer, has a pipeline, i.e., different software components with a well-established flow of operations: in cognitive search, the indexer’s channel is associated with other skills, called mental, capable of implementing the ability to extract information and enrichment of the dataset, to make the contents in the search index or in the information archive more searchable, up to reaching personalized search skills for specific search domains.

The skills used in cognitive research are all artificial intelligence: natural language processing, then entity recognition, language detection and text modification, key phrase extraction, personal information detection, image processing, then optical character recognition (OCR), facial recognition, image interpretation.

These skills make it possible to map “unstructured” data such as images, audio, the same texts with searchable fields and filters: indexing, and therefore cognitive research, will be much more appropriate.

The indexers in cognitive search cross the source data and the index through these “maps”, with data updating: depending on whether they are performed on request or in an automated way, we speak of a “pull” or “push” model.

A use case of multiple indexers, needed for multiple data sources with variations in execution, schedule, or field mapping parameters, is scaling across multiple search areas – multiple copies of the same search index in different areas, each linked to an indexer extracting from the same data source to synchronize the search. Or the parallel indexing of massive datasets requires an ad hoc strategy in which each indexer operates on a particular subset of the dataset.

What are the benefits of Cognitive research, and where is it applied?

Cognitive search allows you to improve the relevance and speed of database searches. It is no coincidence that cognitive research is a part of knowledge mining, the “knowledge extraction” due to AI: deep understanding and immediate exploration of the datasets’ information allows you to identify hidden details and find relationships on a large scale customize queries based on contexts.

Cognitive research, in fact, supports the organization of the corporate knowledge base because, regardless of the type of document or object that contains the information, it correlates and unifies all the information concerning a particular area, creating summaries linked to a specific domain. It is no longer necessary to know in advance where the data is stored, in what format, what to do to access it.

The increase in knowledge relating to processes and the improvement of efficiency in answering questions have the consequences of simplifying activities and optimizing company performance: the speed in obtaining useful information guarantees better user satisfaction. A staff member is a customer.

A high-performance search system considerably reduces the time and effort dedicated to finding information: in addition to eliminating the “dead time” of waiting, cognitive research streamlines all document management and archive organization procedures, with an overall reduction in costs operational. Therefore, the time “freed up” for the management of activities with high added value increases.

Cognitive search systems, also based on machine learning, are designed to continuously improve and adapt to changes in the data entered: a single search function, without many applications, which updates automatically.

In the banking sector, cognitive research helps create instant, 360-degree lists of customers, portfolios, contract objectives, performance, and unified access to information on internal and external data sources, then quickly identifying upselling opportunities. And cross-selling.

In the manufacturing sector , cognitive research allows you to examine data sources, structured and unstructured, and apply advanced analytical models to find meaningful correlations that improve asset management, production control, business security, quality control, monitoring processes, etc least, predictive maintenance. The access and processing of datasets from different sources lead the algorithms not to rely only on the history to make predictions but to be enriched by data from the registers of technicians, maintenance technicians, emails, and images.

Finally, cognitive research is used in marketing to improve regeneration activities, conversion of campaigns, identification of strategies and actions. It helps to understand how customers move in the different points of contact with the product/service. One of the applications of cognitive research is the chatbot, very widespread in the first interactions in customer service.