The word "semantic" refers to the meaning or essence of something. Applied to search, "semantics" essentially relates to the study of words and their logic. Semantic search seeks to improve search accuracy by understanding a searcher’s intent through contextual meaning. Through concept matching, synonyms, and natural language algorithms, semantic search provides more interactive search results through transforming structured and unstructured data into an intuitive and responsive database. Semantic search brings about an enhanced understanding of searcher intent, the ability to extract answers, and delivers more personalized results. In the highly competitive times of today, enterprises need to leverage the enormous amounts of data to gain an edge. This article delves into the concept of semantics and how semantic search platforms based on Artificial Intelligence are a must-have for enterprises.
Search has come a long way with the advancements in technology, and users can now expect far better accuracy and relevancy with the results. 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. Nowadays, data is growing exponentially and a major share of organizational data is unstructured. This calls for a more efficient approach and semantic search is that solution.
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?
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 using in strategic business decision making is one of the priorities of enterprises today, and a semantic search engine can help in the following ways.
- Enhanced Quality of Results: The context-based approach of semantics fetches highly precised and relevant results faster and with high recall (the ability to fetch all the relevant results for a query).
- 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.
- Strategic Decision Making: Nowadays, data is the most important asset of organizations. Because it helps in strategic decision making directly. 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.