The foresight of semantic and graph-based has certainly proven to be more than true. The main driver for this change is the mammoth amount of data that keeps growing. Although, the data is invaluable in countless file formats like audio, video, text, data sources, and email systems. 

Therefore, finding the information on time can be a daunting experience because it consumes a lot of time to search for the relevant information from voluminous heterogeneous data. Especially for employees, it’s frequent to face these scenarios where they type keywords in the search bar only to be served by irrelevant documents that don’t cater to their searches. And, this happens almost daily in every organization.

Don’t worry! 

AI-based intelligent search eliminates this annoyance. It works on semantic search. 

What is Semantic Search?

Semantic search is understanding the meaning behind words by understanding the context in which they are used. While doing so, it understands the intent and context of a searcher’s query by adding a semantic layer of data across all of your documents in your company, regardless of file type and location.  

Globally known examples of intelligent search are search assistants of electronic devices – Siri for Apple and Google Assistant for Google. They understand the intent behind a query asked by the user in a conversational style, not just keywords. 

So, when this approach is applied in a business environment, AI-integrated machine learning uses search and text analytics that imply new NLP to understand your industry’s language. Also, it saves time on research and helps them discover meaningful insights from text, video, web content, documents, and other sources at the enterprise level. 

Finally, semantic search aims to give more relevant, concise search results to save time, build efficiency and erase discovery annoyance. Besides, it helps organizations to bring efficiency and improve workflow while creating more dynamic and precise analytics. 

You must be thinking about whether replacing your legacy system by future-proofing it with semantic search is an ace idea? 

Precisely, a company would not be making decisions just to streamline and boost the efficiency of the workflow by integrating new-age Enterprise Search Results but also keeping the data safe and secure. That’s not all! 

Let’s uncover some ways in which the Semantic Search engine offers tangible benefits to an organization at an enterprise level. 

Unskippable Benefits of Semantic Search to an Organization at an Enterprise Level

• Explore Hidden Insights 

The explanation of company text data and documents integrated with semantic information allows your employees to find all metadata associated with any given entity. Knowledge workers can also search by market capital or industry type for businesses, or can even search by nationality and gender for politicians. 

Notably, more than 100 metadata properties can be easily searched – and all are automatically determined by agile, dedicated, and deep semantic search.  

• Automated Semantic Analysis 

The next benefit Semantic Search offers is automatically annotating company text data and documents with semantic information, eclipsing related entities, topics, and entity-specific metadata. 

• Make Powerful Queries 

State-of-the-art Semantic Search comes along with a powerful query language that let you find the exact data in any preferred language. Regardless of the fact if your documents are in Arabic, Chinese, or English, the deep search technology has the potential to address queries in one specific language and provide results in another language. 

Using deep search technology, the searches that require complicated boolean queries and complex text mugging turn easy. Therefore, using a simple interface, the solution can easily perform queries like:

“Find all documents mentioning an automobile brand or an apparel company positively”

Or

“Find documents mentioning business with a market capital more than $ 100 billion and whose stock scale high by 10% in last month”

As a reliable sentiment analysis tool adds more languages, entities, and metadata, the combinations of queries also grow with the possibilities becoming endless. 

Finally 

Semantic search has the potential to fill in a lot of blanks, so uploaded data in an easy-to-access digital format will bridge that gap. The question that still prevails is “How long will that take?”

For my part, I’m betting that semantic search will be an inescapable part is not so far future. So, sooner would be better.