It all started in 2013 with the hummingbird. Never heard of it? No problem. Google is known for naming large and fundamental algorithm updates after animals. After Panda (2011) and Penguin (2012), Hummingbird (2013) followed and ushered in the age of semantic search.

In short, Google has been trying to identify, understand and evaluate relationships ever since. Specifically, it is about the relationship of so-called entities. What this is exactly, Google explains itself in the corresponding patent:

An entity is a thing or concept that is singular, unique, well-defined and distinguishable. For example, an entity may be a person, place, item, idea, abstract concept, concrete element, other suitable thing, or any combination thereof.

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What are entities used for in SEO?

In search engine optimization, we use entities for semantic content optimization. For example, if we want to write about the Oscar, then other entities already exist in Google's knowledge database that are related to the Oscar. If these entities appear in the text, Google understands that our text is relevant to the term "Oscar".

In the case of the Oscars, these would include: movie, stage, winner, trophy, award, actress, red carpet, gary oldman and emma stone (source: entity explorer).

At the same time, we convey to the search engine that our text is not about another meaning of the word "Oscar". According to Wikipedia Oscar is also a legendary figure from Irish mythology, a city in the USA, a satellite, a car brand, a character from Sesame Street, and various Brazilian soccer players.

In the following, five tools are presented to find relevant entities and semantically optimize your own content. I present all tools using the Oscar example.

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#1 Entity Explorer

The tool can be found at entityexplorer.com and is really very simple. The short explanatory video on the home page is not really necessary, because it is aimed at intuitive use. Here is the result for "Oscar".

The colored arrows can be moved as desired. The way of displaying can thus be adjusted and the result can be sorted according to one's own preferences. In addition, further entities can be displayed in addition to those already found. The result can be easily downloaded via the button "export image".

The tool also works in German. However, the term must be clearly assigned to the German language, which is not the case with Oscar.

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#2 Extractor de entidades

This tool is a browser extension in combination with the Dandelion API. Alexander Rus explains how exactly you can set it up. in his video The fact that the browser extension originally comes from Spain, as you might have guessed from the name, has no influence on the result.

Once you enter a search term on Google, you simply click on the browser extension and it will show you the found entities directly in the SERPs.

In addition, you get a summary of the most frequently found entities on the right side of the SERPs. These can in turn be exported as CSV files via the corresponding button "Exportar a CSV".

The tool also works in combination with a site query. Let's say you have already published your article and now you want to find out the entities. Then you simply search for site:my-domain.com/article and then click on the browser extension. This only works if the URL is also in the Google index. Let's imagine that I own the domain dw.com, then my result would look like this.

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#3 Google Docs & Google Natural Language

These two tools come directly from Google itself. On the one hand we have Google Docs, so to speak the MS Word from Google. Prerequisite for the use is a Google Account. On the other side we have the demo of the Natural Language API.

Both tools can be used to explore either the entities in one's own text or those of a foreign text.

Although most people probably know how to use Create and edit Google Docs, this feature is often overlooked. In Google Docs, you simply copy the text into a blank document. As an example, I'll use the introductory text of the Wikipedia article on the Oscar (Academy Award). The "hidden function" to display the entities can be seen on the screenshot where the red arrows point to.

After that, a kind of knowledge graph opens (depending on the topic of the text, less information may be available). If you now click on More next to Topics at the top, you will get a list of entities that Google has read from the text and about which you have written.

The Natural Language API demo works on the same principle. Simply paste the text into the field provided and click ANALYZE.

The result is again a list of entities sorted by salience score. The salience score has a range from 0 to 1. The higher the salience score, the higher the connection of the text to this entity. In the example, the entity "movie award" has the highest score with 0.16, which of course makes sense in relation to a text about the Oscar.

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#4 AlsoAsked

At alsoasked.com you can find a tool that helps you to find the relevant questions about an entity. You probably also know the "users also ask" box that occasionally appears in search results.

These questions have been generated by the algorithm itself from its own understanding. So the search engine shows us which questions and topics it wants answered about the entity we are looking for.

As you may know, as soon as you click on one of them, new questions open. AlsoAsked helps you to understand the connection between the questions, visualize it and cluster it. It is important that you also select language and country correctly.

The result shows a kind of flowchart that can be read from left to right. As you can see, the last question "How old is Oscar?" is no longer aimed at the movie award, but at real people. So these (and the following) questions would be semantically unsuitable for a text about the film award Oscar.

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#5+1 Wikipedia + Google Image Search

Finally, two quick wins. If a Wikipedia article exists for a term, it is worth taking a closer look at it. It is no longer a secret that Google draws a lot of information from Wikipedia and learns from it. Not least via the internal links and the article structure, many relationships can be developed for the search engine.

If we look at the introduction, we see that there is a high overlap in the anchor texts of the internal links and the entities from Google Tools (Google Docs and Natural Language API).

The Google Image Finder can also be used to discover one or another entity. This is about the suggestions that can be seen between the search bar and the images.

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Conclusion

Entities are a nice thing to semantically optimize your own content and tell the algorithm: Hey look, my text is extremely relevant to this topic. Nevertheless, SEO still consists of many individual disciplines whose meanings should not be neglected. Rather, keyword and entity optimization should go hand in hand. Finally, it remains to be said that one should not forget to turn on one's own head in the process. If you do not optimize bluntly according to any tools, but use the tools much more for support and continue to work user-centered, you can expect success in the future.

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