Semantic search is redefining search and how! Today, enterprise search tools are all based on semantic search in order to achieve higher relevancy and accuracy. This article takes a closer look into how it has changed our approach to search.
Search has come a long way with the introduction of advanced concepts and technology and with this, what we expect from it has also evolved with time. Now, one might wonder what was wrong with the traditional search approach, which was primarily based on keywords. Well, the simple answer is the lack of accuracy, as the keyword based approach can often go wrong. This lack of consistency can serve the purpose of the average user. However, when it comes to enterprises depending on the accuracy of the search to gather valuable insights for their business, there is a need for a more advanced search tool.
Moreover, what makes enterprise search more complex is the fact that it deals primarily unstructured and unorganized data. Gathered from multiple sources, the data is present in various formats, including social media, emails, images, documents. While all this data contains key insights that can be used to formulate important business decisions, it only adds to the complexity of the search process. This is where Semantic search, which is based on principles of artificial intelligence (AI), can step in as the solution to the challenges of the traditional keyword based approach.
How Semantic Search is Different
Now, in order to get a clear idea of how AI based semantics can transform the search approach, we must look into how it works. Now here’s the basic difference:
Keyword based search works by matching the query term with its density on a page. In other words, when a user types in a query, the platform starts looking for pages, documents and other sources for the presence of the term. The ones that have the query phrase in them are displayed in the results.
For example, if a user types in ‘test automation tool’, the results displayed through a keyword search will have the key phrase in them. While this often serves the purpose, its biggest shortcoming is the fact that it cannot display results with similar terms, such as ‘software QA automation’, ‘automation software for software testing, which do not contain the keyword but are very similar in meaning and essence.
However, semantic search is all about deciphering the meaning of the query term and then displaying results that are not just relevant to it, but also caters to a broad range of its synonyms. Thus, semantic technology significantly improves the scope of search and brings up results that are never displayed in totality by the traditional approach.
Note that synonym search is something that can be achieved with pure machine learning technologies, but is an exhaustive process that is time consuming and requires a lot of effort in training the machine to learn the set of synonyms. Moreover, pure machine learning has limited use in the absence of artificial intelligence.
Semantic technology that is based on AI is equipped with the capability of understanding the human language, as well as decipher the context of the query. Thus, semantic tools get the power to understand the meaning of the query term and its context, to show up the most relevant results, irrespective of its presence or absence.
The Key Advantages:
Now that we are clear about how it works and the possibilities it offers, let us sign off by looking at few of its advantages that have the potential to help your business gain the competitive edge.
Semantic search is redefining our approach to search and its ability to provide us with relevant and deep insights. Today, enterprises are awakening to the importance of investing in a semantic search platform for their business.
Semantic Search, Keyword Based, Query Term