The amount of data on the Internet doubles every two years and it’s becoming a tangle of info. This is why search engines like Google aggressively developing its web crawler algorithms that emphasize giving its users more relevant information and ranks websites based on this – and SEO marketers leverage on their understanding of this as its primary means of pushing its online advertising strategy. 

One approach that Google takes is developing an algorithm that recognizes search queries made by regular users in a conversational style, called semantic search. The machine learning algorithms that characterize and give Artificial Intelligence software like SIRI and Evie that “personal” and “conversational” experience is largely the brainchild of google semantics search algorithms.

What is Semantic Search?

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Eviebot is an example of a machine-learning A.I.

Semantics in language refers to understanding the meaning behind words and sentence structure. In search engine terms it refers to the ability of search engine algorithms to make sense of the intent and contextual meaning of search queries, both structured and unstructured, to best create search results that match.

For example, a search on “best food” will result in page of restaurant reviews, food guides, and a short list of “Searches related to best food” to help users narrow down their searches – or even suggest new areas of interest. This is what allows relevant results to appear in a search results page even though they didn’t specifically used in the query.

Why it’s important to search engines.

To put it simply; relevancy and accuracy. The amount of data floating around on the web means that it’s necessary to better understand what users want (at times even before they even know how to). This helps filter out spam while improving user experience.

Semantic search helps search engines identify and flag spam through techniques such as:

i) Latent semantic analysis (LSA),

A technique that analyses synonyms located inside documents and the patterns and distribution of words across multiple documents while eliminating natural language redundancies (grammar words, for example).

ii) Latent Dirichlet Allocation (LDA),

This technique is used to automatically discover topics that are relevant by analysing the number of topic keywords in the current document, translating it into probabilities, and then matches them with other documents with the highest resemblance.

In Natural English (pun wholly intended) what this means is that it lets Google associate strings of characters (a.k.a. words) with topics and information that’s more likely what users are looking for.

What it means for SEO and Online Advertising.

SEO specialists/SEO agencies should learn and adapt their methods to include Semantic Search criterion to improve SEO results. Demonstrating expertise and knowledge about a topic is key to playing along with semantic search for better SEO rankings. Here’s how it’s done:

1. Good content.

One key factor that Google uses to rank sites is the amount of activity. Good content is an important component of online advertising. Useful articles drive new and returning traffic to websites (e.g. Huffington Post, WikiHow, etc.).

And more users means a higher chance that discussions and exchange of ideas, which has the potential to spike the level of activity. Google loves site activity which is highly regarded in its ranking doctrine. Becoming an authority in your niche is important as Google uses such sites as points of reference.

It is recommended that build a website with a strong focus – a brand name or go-to place for anything related to that niche. Keeping your content focused helps build traffic from organic search because of returning users who come back expecting more of a good thing. Think blog sites like Live Science (covering popular science topics) and WebMD (medicine and health).

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Google Knowledge Graph Card.

2. Use Structured Data Markup.

Structured Data Markup helps Google describe things on the web and how they are categorized. This data is provided in HTML and AMP pages. Structured Data Markup makes your content show up on Rich results, which greatly helps improve SEO results. If your site is seen by Google as a highly factual site it gets the added bonus of being added to the Knowledge Graph cards. This greatly improves click-through-rates (CTR) from organic searches.

3. Include Internal Linking.

Internal linking can improve user experience by creating a natural flow for reading and highlighting important, relevant information that they might miss otherwise. It’s also a great way to get readers to peruse relevant information on other pages on your website. Just remember that you should always try to keep the links to relevant to the reader.


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