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Cognitive Search Vs Elastic Search: It’s Main Differences?

Find out what distinguishes our solution for enterprise search and our personalization-focused approach. The Search for documents and information in the company is a crucial issue for the success of projects, the efficiency of processes and, in general, for the management and growth of the business. Every second we work, we produce files, materials and procedures which, over time, risk becoming part of submerged knowledge, a phenomenon which risks making companies blind to what has been done.

An advanced search system can make all the difference when you need to find a document or piece of information crucial to making a decision. There are numerous solutions on the market; today, we want to talk to you about this. Not all are the same, indeed. Knowing the differences helps you make a more informed choice, which gives results over time. That’s why in this article, we want to tell you ten differences between our Cognitive Search solution and Elastic Search 

Depth Of Detail: From Document To Paragraph

Advanced search solutions are born to help people find the right information at the right time. The depth of detail, therefore, becomes a crucial element. After searching for information, ElasticSearch can report the document that contains it. Cognitive Search, on the other hand, takes advantage of the double indexing feature to support searches: it finds the document and returns the paragraphs containing the information sought. This saves time and makes it even easier for people to search.

Use Cases And Implementation

ElasticSearch is widely used for e-commerce or as a search engine on a customer service portal. Over the years, it has evolved to provide a standard solution for these fields. Cognitive Search, by contrast, embraces a personalized approach. There are no ready-made packages because the companies that work with us are looking for specific solutions that often require particular functions and do not need the many small additions that standard services offer but which risk remaining unused or, worse, increasing complexity.

Type Of Connections: Web And Legacy

Every company has a different ecosystem of tools and applications. That is why it is important to evaluate the type of supported connections when evaluating the most suitable solution. ElasticSearch focuses predominantly on web-type containers such as Microsoft 365, Salesforce, Google Workspace, Jira, Confluence and GitHub. In addition, Cognitive Search offers connectors to legacy document systems such as Documentum. This choice also allows us to support companies whose systems do not necessarily travel on the cloud, as in the case of the project with Saipem.

Autocomplete System In The Research Phase

When people search on Google, the search engine automatically provides completion options. The same is possible in an advanced enterprise search system: ElasticSearch provides a preset auto-completion system based on the standard natural language used by Google Cloud for developing its products and services. Cognitive Search, precisely because it was designed to model itself completely on the reality that welcomes it, is structured differently.

Two companies can call the same thing with two different words or abbreviations, so the autocomplete system must also be adapted. The auto-completion is then adapted, in the development phase, based on the language of the single company through the use of indexes and lists created ad hoc on the client’s vocabulary.

Search Synonyms

What happens if people search for a word on ElasticSearch? The system also returns all results that contain its syntactic synonyms. This functionality is essential for bringing out the documents that the company may not know it has and which are part of the hidden knowledge. Cognitive Search combines syntactic and semantic Search for synonyms. Its search engine is also based on the concept, on the value of the word in the context, and not only on the one-to-one relationship between the various terms. Also, in this case, the difference is expressed in terms of the accuracy of the result.

Custom Front-End

Any data becomes useless if people fail to read and interpret it. For this reason, the visualization of the results is an important step in the research process. In all of this, the front end plays a fundamental role. ElasticSearch offers its customers a standard front-end that can be customized through the APIs provided, effectively developing its front end separately.

While this satisfies those who need basic functionality, we have seen that for Cognitive Search, the approach is completely different. That’s why the complete customization of the front end based on the company’s indications is immediately included within the development project. The target? Highlight and make accessible what is most important and limit the rest, to reduce the noise.

Multiple Semantic Models

To give people a result that is as relevant as possible to what they want to find, semantic Search comes into play, i.e., the algorithm’s ability to interpret the Search’s context and personalize the result based on the intent of the person who performs it. ElasticSearch makes its standard semantic model available to companies for all searches. At the same time, Cognitive Search allows you to choose multiple semantic models each time, for each project, to be used together to maximize Search by concepts.

Automatic Tags And Metadata

Classifying a document is essential to be able to find it again. The search engine interprets people’s requests and compares them with the descriptions, tags and metadata of each document in its database. Once a correct association is found, it returns it as a result. Cognitive Search, unlike ElasticSearch, supports businesses by providing automation of tags and metadata that help classify the document and supports dual indexing. Based on its content, the file is scanned and classified without the company and people having to intervene manually.

Text Snippets Among The Results

Have you ever noticed the look of Google pages? After you have searched, the engine selects what it thinks is right for you and presents it as a ranking. The element we want to highlight, however, is the snippet. The snippet of a single result on Google enriches the simple title with more information that the person needs to decide whether or not to visit the link. In practice, they are road signs on the web that make search results more informative.

As we said before, how we view information matters. For this reason, in Cognitive Search, in addition to the classic ElasticSearch functions, each result is introduced in the form of a snippet, thus providing a more user-friendly interface that helps people understand which documents to consult first and where to find the right information. A preview appears inside the snippet of the file and the block of text in which the system has identified the answer to the search query.

External Integration With A Virtual Assistant

Integrating a Virtual Assistant can facilitate the Search for information within the company by reducing the relative times. People need to write or use their voices to ask questions. It will then be the task of the Virtual Assistant to interpret the natural language and return the most relevant result. Currently, the direct integration of a Virtual Assistant is not present among the standard packages of ElasticSearch. In the case of Cognitive Search, however, the situation is completely different. Since each solution is designed from a customs point of view, it is possible to prepare all the necessary integrations right from the start.

Also Read: BigQuery Omni Is Now Also On AWS And Azure

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