Sematic Search: What? How? and Why?
Introduction
The role of semantic search is to understand the underlying context behind a quarry made by a user. Language in its full form is literal, or words have multiple meanings depending on the premise behind it. In keyword searches, the queries are matched and ranked according to its literal interpretation and therefore can often neglect other relevant information. While communicating with private databases a keyword search may prove very useful, but in terms of generating a big picture of the user’s quarry, it fails to meet the demand. Semantic search utilizes Machine Learning, and Natural Language Processing (NLP) to generate searches, which means it encompasses all the data that the algorithm is trained on through machine learning, and communicates it with the vector embeddings initiated with those similar databases. Which leads it to find multiple probable searches that may line up well with the user’s quarry.
Behind the scenes of a semantic search
Semantic search engines use indexing algorithms which create a set of vector embeddings in accordance to the context it may share. In semantic search, contexts can be any information relating to the user’s quarry. It may be the user’s geographical location, the similar entity of search, the user’s search history, or the underlying context behind the search.
For example, if on a food delivery site a user searches for spicy noodles, it may interrupt the quarry “spicy” with similar words such as: “hot, chili, naga,” etc that are similar to the requirement. Also in contemporary cuisines, a similarity between different types of “noodles” or “ramen” would also be in consideration. These simple two words can also be linked to the type of restaurant, the type of cuisine, the nearest open location, even the reviews that were expressed as spicy or hot in those particular foods.
This means there is a great chance of meeting the user’s demand or quarry. The user will have a more diverse option in his particular search and therefore is likely to be satisfied. This enables a more intuitive experience which is the goal of any service.
Importance of Semantic Search
Semantic search improves on learning as it uses machine learning. It has key performance indicators such as conversion rates, and bounce rates. It will follow these trends and learn which keyword searches will be never able to do. Keyword search uses scoring algorithms and searches from sorted databases to match the exact quarry. Therefore different types of files are never correlated in their shared premise. While keyword searches may use certain quarry relaxation tools to maximize typo tolerance, synonyms, browsing history etc. But it can never understand the user’s intent.
There is a place for keyword search when seeking specific entities from a static database. But these are not scalable in terms of variety and usability. Keyword search only matches the literal words that are told in the quarry, and then matches them from their dataset. What Sematic does is much beyond just a basic search. It learns from patterns, it personalizes with the user, understands deeper premises, interprets the human premise, and predicts the user’s inquiry far better.
In conclusion, It may be better for an enterprise to use keyword searches to manage their internal database and communicate effectively with it. But to interact with large sums of data and to be more customer oriented, there is no alternative to semantic searches
