Search has come a long way with the advancements in technology, and users can now expect far better accuracy and relevancy with the ai writing detectors. Today, while traditional keyword based search is still relevant, there is technology based on artificial intelligence (AI) that is redefining our imagination. The biggest challenge while working with keyword based approach is that it is not always accurate when working with unstructured data. Today, data is growing exponentially and a major share of organizational data is unstructured. This calls for a more efficient approach and semantic search is the solution enterprises need today.

The Challenges of Keyword Based Search

According to a study conducted by IBM, Stanford and MIT, search can be classified into 2 types – navigational search and research search. The former involves looking for a specific document or page and this is where the traditional keyword based approach works wonders. However, the latter involves acquiring every bit of information that is relevant to a specific topic. This is where the traditional approach often fails. It’s solely based on the presence of the keyword, which sometimes is not enough to fetch all the relevant information.

Why Semantic Search is the Solution

Now, the question that arises is, ‘How semantic search can make a difference?’ Well, let us look into the concept and how it works, in order to understand how it’s different from the traditional approach. The word ‘semantic’ means ‘meaning’ and semantic search engines use AI-based algorithms to understand the concept of the query for each term in order to showcase the most relevant results. In other words, it is all about results that are in context with the query.

The semantic search engines use advanced cognitive features to understand the meaning of the query, and this opens new possibilities in relevancy and accuracy of results. So, when you type in a query, you get all relevant information, including the ones that feature synonyms and related terms. This broadens the scope and is great when you are working with data that is unorganized and unstructured. Enterprise data is mostly unstructured, with data collected from different sources, including email and social media. Unifying this data to draw insights for use in strategic business decision making is one of the priorities of enterprises today, and a semantic search engine can help in the following ways.

1] Enhanced Quality of Results

The context-based approach of semantics fetches highly precise and relevant results faster and with high recall (the ability to fetch all the relevant results for a query). 3RDi Search is an enterprise search platform that is powered by Retina API, its own NLP, text mining, and text analysis engine.

2] Improved User Experience

The ability to comprehend natural language powered by AI leads to an enhanced user experience like never before. Coupled with a user-friendly and intuitive user interface, it can redefine the way users interact with a business. Coveo is another good example of a platform that is powered by out-of-the-box machine learning for a user experience like never before.

3] Strategic Decision Making

Data is the most important asset to organizations these days because of the information it holds that can be used in strategic decision making. Semantics helps enterprises to unleash the potential of this data and derive insights to fuel business growth.

Semantics is growing in popularity, with more and more organizations leveraging its power in enhancing the search experience. This establishes its importance in effective enterprise search.