The process begins with concept Centrifugal Disc Finishing Machine

The process begins with concept Centrifugal Disc Finishing Machine identification which identifies the concept of the query. The sentence boundary detection recognizes the start and end of the sentence. Next, POS tagging tags the words in the sentence into their respective parts of speech. Then the words are matched with the in-built domain vocabulary (ontology) for a match. Next the text is analyzed for Named entities with Named entity extraction and they are next chunked together. Next we have Query Expansion in which the query is reformulated to improve retrieval performance. At this stage, it is decided which query will be shown higher up on the results page. Finally, the query is subjected to sentiment analysis and the consolidated and semantically rich results are presented to the user.

Search can be classified into 2 broad categories – navigational and research-based. While the former is used to help the user navigate through a website or an app, the latter is used to fetch documents and information for a specific purpose, research being a primary example.

Semantic search has its application in research based search, and is highly relevant in two major domains today – medical research and enterprise search. Both these domains have a comprehensive collection of data that needs to be analyzed for information retrieval.

Needless to say, the every successful enterprise search tool and the tools used for research are based on semantic search.

Semantics is derived from a Greek word which means ‘meaning’. The semantic concept began in 1971 with Robert W. Floyd. The source of semantic search, as we know it today, is a result of the evolution of the semantic web that was built using the principles of ontology. It all began with a paper written by R. Guha published in 2003. However, it was only in 2013 that it saw its first application and it has created history ever since.

The most rewarding experience of using a semantic search-based platform is that it goes on to redefine the user experience by understanding the intent of the user when he is typing in a query.

With its introduction, it has become not just possible but easy to deal with the large quantities of unstructured data that is common to enterprises. Even in the field of research, it has made it possible to dig deep and get access to more information than ever. In other words, it provides the user more information than he expects, thus increasing the user satisfaction.

Before we sign off, we shall leave you with another fact. It’s interesting to note that the most basic difference between semantic search and normal search is the higher level of accuracy that the former offers. It goes beyond the exact words you type in to fetch you results that normal search can never provide.