Semantic search, also known as concept search, is a technology used to help users find the right information by understanding the user’s intent and the contextual meaning of their query. Semantic search goes beyond traditional keyword-based search by understanding the relationships between concepts and the context in which they are used. This allows semantic search engines to return results that are more relevant to the user’s needs.
How is semantic search used outside of Text Analytics?
The term “semantic search” is sometimes used more broadly to refer to any type of search that takes into account the meaning of the query, rather than just the literal keywords. This includes technologies such as natural language processing (NLP) and latent semantic indexing (LSI).
What is the difference between semantic search and other similar terms?
Semantic search is often confused with other similar terms, such as Natural Language Processing (NLP) and latent semantic indexing (LSI). However, there are important distinctions between these terms. NLP is a subfield of artificial intelligence that deals with the interpretation and understanding of human language. LSI is a technique used to improve the accuracy of keyword-based search by taking into account the relationships between concepts. Semantic search combines aspects of both NLP and LSI, but it is not limited to either one. Semantic search engines aim to understand the user’s intent and the contextual meaning of their query in order to return results that are more relevant to the user’s needs.
“Semantic search” is also sometimes used as a synonym for “ontology-based search”. However, ontologies and semantic networks are two different types of knowledge representation systems, and ontology-based search is a specific type of semantic search that uses an ontology as its knowledge base.
Search engines and semantic search
Semantic search is a relatively new technology, and there are not many semantic search engines available on the market. However, some traditional search engines are beginning to incorporate semantic search features into their algorithms. For example, Google’s Hummingbird algorithm, released in 2013, is designed to “understand the intent of queries” and “improve the relevance of results”. Similarly, Microsoft’s Bing Search has been using semantic search technology since 2009.
The future of semantic search
As the world becomes increasingly digitized, the need for effective search engines that can help users find the right information quickly and easily will continue to grow. Semantic search is an important step forward in meeting this need, and it is likely that more and more search engines will begin to incorporate semantic search features into their algorithms in the years to come.