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Semantic SEO is a marketing technique to improve the traffic of a website by providing search engines with metadata and semantically relevant content that can unambiguously answer a specific search intent.

In 2011 as Google and other search engines began moving towards Artificial Intelligence and natural language processing to understand the searcher’s intent and the meaning of a query they started to work with entities and concepts rather than parsing questions and web pages using keywords.

As search engines got smarter and they started to dive into the real meaning of words, content owners have begun to move from creating web pages to describing these web pages using linked open data and semantic web technologies. This has become possible with the creation of the schema.org vocabulary, an initiative launched in 2011 by the world’s biggest search engines (Bing, Google and Yahoo!) to implement a common data schema structure to describe web pages. On 1 November 2011 Russian largest search engine Yandex also joined the community.

An overview of the schema.org vocabulary

Why Semantic SEO?

In a nutshell, search engines need context to understand a query properly and to fetch relevant results for it. Contexts are built using words, expressions, and other combinations of words and links as they appear in bodies of knowledge such as encyclopedias and large corpora of text.

Semantic SEO is a marketing technique that improves the traffic of a website by providing meaningful metadata and semantically relevant content that can unambiguously answer a specific search intent. It is also a way to create clusters of content that are semantically grouped into topics rather than keywords. In a famous Google patent on context-vectors, an example with the word “horse” is provided. Same word but with different meanings in different contexts: a “horse” is an animal for an equestrian, a working tool for a carpenter, and a sport equipment for a gymnast. In Semantic SEO, much like Wikipedia does, content is cataloged and organized around each context in such a way that machines can understand and value its uniqueness.