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What is Semantic Search and Why it is Important?


 What is semantic search?

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. Google’s Knowledge Graph is a paradigm of proficiency in semantic search.

Why do engines pursue semantic search?

From an engine’s perspective, it’s not hard to imagine why Google would want to pursue a more connected world: more data, less spam, a deeper understanding of user intent, and more natural language (i.e. conversational) search. Understanding all of this data maximizes the possibility of their users getting the best search experience possible.

With the world’s data doubling every two years, big data has become the norm for players in the online realm. All this data creates an overarching concern of “What does this mean to me?” The process of organizing, structuring, and semantically connecting data is a coveted role for search engines.

One of the ways that semantic search helps Google is by identifying and disqualifying lower-quality content. Methods like article spinning and keyword stuffing are more easily flagged due to advanced systems such as latent semantic indexing (LSI), latent Dirichlet allocation (LDA), and term frequency-inverse document frequency (TF-IDF) weighing schemes, which use term frequency and their predetermined weighted relationships to determine quality. This means that search engines have a good idea of what words statistically occur together and make semantic correlations, which can be used in the war against spam.

Using semantics and entity-based search, engines can gain a better understanding of what users may want. For example, the image below shows a simplified illustration of what the data in an entity-based search algorithm would contain. It includes entities (people, places, things, concepts, or ideas) which are represented as nodes, and connected by their relationships as the arrows. The diagram shows how entity-based search seeks to connect various entities, in this case the individual Simpsons characters, which creates more depth to search responses.

Illustration of a semantic taxonomy of entities (as nodes) and attributes (relationships).

Knowledge Graph answer driven by the entity-based search associations.

Semantics help to understand more completely what our searches mean today. For example, a search for [Jennifer Lawrence] is most likely related to the American actress, star of the Hunger Games, and fashionista. Google provides news, photos, facts, social media accounts, and movies all related to Jennifer Lawrence. Through understanding entities, and coupled with the perplexing amount of data behind the habits of the 7.4 MM searches for Jennifer Lawrence, search engines can gain a better understanding of what the next user will want. Google’s invention of the Knowledge Graph is a golden example, aiming to understand things, not strings.

Search for “Jennifer Lawrence” appears with only topics related to the actress.

Google, and other engines, have become very adept at recognizing different entities and formulating answers to questions. And it’s through this connecting of data that search becomes stronger. Answers to questions are algorithmically understood and displayed when, for example, one searches “who is the dancer in the chandelier video?”. Google “knows” that it is Maddie Ziegler. The idea that a search engine can connect the keywords to an entity and reply with the accurate answer makes Google’s search much more constructive for its users.

Knowledge graph answer with entity name not mentioned in the query.

Semantic Search SEO Implications

For SEOs, understanding semantic search has some major benefits. A large part is the ability to remain ahead of the curve. Search engines are moving forward and as SEO experts we need to make sure to stay at the top of our game. Semantic search is going to become especially important as voice search gains more traction.

The method of integrating semantic search signals has huge implications about how we approach our SEO strategies. If we could know all of the topics and keywords associated with a particular entity, we could create perfect content and achieve the optimal rankings for our clients. Although we live in an entity-not-provided world, there are a few tried and true strategies that can enhance your semantic search strategy.



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