What is Semantic Search?
Semantic search is the analysis of user behavior and identity data points that occur when using a search engine. This user behavior creates a network of relationships between words, grammatical rules, and entities. Search engines like Google, Bing, and Yandex rely on this network to understand the meaning of terms and concepts in a search query, and to subsequently provide a list of websites that contain content that can meet the search intent behind it.
The use of natural language processing (NLP) and machine learning (ML) algorithms has enabled search engines to interpret user queries in a more human-like manner, and to provide more personalized answers to each user.
This focus on the user intent is the main difference between lexical search and semantic search.
Key Concepts in Semantic Search
Modern search engines use Entity-seeking Queries, Canonical Queries, and Query Rewriting methods to associate different variations of queries used to search for a specific fact or entity and provide an accurate SERP focused on the search intent.
- Entity-seeking Queries are used to mine the knowledge graph or knowledge database and find the correct entity.
- Canonical Queries create the connection between the different keyword variations to search for any given fact or entity.
- Query Rewriting is the process of creating a final query that serves as the main search intent, as well as any other queries that serve as secondary search intents. It can also adjust the final query to personalize the search results page (SERP) based on location or other parameters..
The evolution from lexical search to semantic search.
The first versions of search engines would rely on a lexical search model by grouping content through the main keywords and keyword variations and they would use backlinks to measure the popularity and quality of the content.
But relying only on keyword frequency and backlinks made it easy for people to manipulate this ranking method. Allowing low quality content to rank and therefore undermining the main objective of a search engine, which is providing the best results to answer the user query.
The lexical search model then evolved to group content not only by the use of the main keywords but to also look for synonyms, antonyms and homonyms. By learning how those lexical elements are used to talk about a certain topic, search engines began to create the semantic networks that connect entities.
HTML Element and Semantic Content Structure
Since search engines discover and consume content through web crawlers, semantics in the HTML can also help search engines improve how the content gets indexed and understand how to display it back inside the SERPs.
Connecting different content types to different entities enables search engines to offer SERPs that feature appropriate rich results for each query.
Image packs, people also ask, knowledge panels, product carousels are all examples of rich results that hope to satisfy the search intent in the best way for each user.
Structured Data like schema markup provides an extra layer of information that search engines and other types of web crawlers can use to improve the process of discovering and categorizing the content in the web. And this type of markup is also required for the content to be featured in some of the rich results in Google’s SERPs.
Why is Semantic Search Important for SEO?
Semantic search made it possible for modern search engines to exponentially improve the quality of the SERPs by focusing on the search intent behind each query instead of just connecting content by matching keywords together and counting backlinks as a measure of content quality.
Understanding these semantic connections will also be very important to know how to optimize content for chatbots and other conversational search models.