The 'Entity'-Based Search – Is It the End of Keyword Research?

Search engine optimization has been based on keywords from the start. However, with Google's ability to semantically analyze content, the fundamental conditions are changing. The Knowledge Graph showed the paths that the future of search is likely to take, but this is far from the end. What this means for SEO and keyword research and optimization is discussed in this blog post.

In the past, the process of a search query was relatively simple: A user entered one or more search terms into Google's search box and received results based on these keywords that Google's algorithm deemed relevant. However, it hasn't been that simple for some time, as the Venice Update has surely demonstrated.

Google local results

Explicit and Implicit Influences on Search Results

Besides the explicit aspects of a search query, such as the terms directly entered into the search box, implicit aspects are becoming important, which also help determine the results: What device is being used to search, at what time, in which location, in which city, in which country, and what does the search history of the searcher look like?

On Moz.com, Tom Anthony summarizes it with this formula:

Search query = explicit aspects + implicit aspects

But what if this is just the beginning? Is it conceivable that in a few years, search results will no longer be based on search queries, but that implicit factors will determine everything? The app Google Now could offer a glimpse into the future, as this is exactly what happens here: Users are presented with information tailored to their behavior and habits, without entering a keyword-based search query: “You receive cards with useful information for your day-to-day life – even before you search for them.”

Google-Now

Various tests and updates in search results and the search algorithm in recent years ensure that the list of results today stands out significantly from the purely website-based results of the past and gives a hint to the future:

• Personalized search results: locally tailored results and the integration of content from the Google Mail account

• Social signals, the integration of authorship, the topic of Author Rank

• The identification of structured data and the integration of rich snippets into search results

• Google's increasing understanding of natural language compared to purely keyword-based search queries

It shows: Understanding instead of indexing is Google's goal. The Knowledge Graph is just the beginning of this. The ultimate goal could be entity search.

The Future of Entity Search

In an interview with Barbara Starr, an expert on semantic search, on search engine land, the expert hints at what the evolution of semantic search could mean for the future of SEO. Already with the introduction of the Knowledge Graph last year Google indicated that it is no longer about pure strings. Instead, search is increasingly based on the interpretation of “search objects.” To interpret search queries in this way, Google creates entities, an “entity graph”, that maps the meaning and relationships between terms. The basis here is, on one hand, the interpretation of the intention of the searching user, and on the other, the user's context: Where are they located, what device are they using, etc.

A purely entity-based search would mean for websites: Those not recognized as entities will not be found! The conclusion from this is that SEOs in the future would be responsible for not viewing their content through the keyword lens, but instead creating machine-readable entities that answer firmly defined search queries. Semantics and structured data are the important keywords here.

The Webmaster Tools give a hint of the increasing importance of structured data, as they have been evaluated there for some time:

Structured data in Webmaster Tools. A small preview of entities can be found, for example, in the “Top Charts” in Google Trends. Here, search queries are already assigned to a (currently quite manageable) number of entities.

In a purely entity-based search, the use of keywords on a website would no longer be important; it would even be entirely ineffective as long as Google does not recognize entities on the website.

Of course, it is not (yet?) that far. But it won't hurt to start dealing with markups and schema.org annotation early. Even if not all markups show a visible effect in search results yet, it is wise to use as many relevant markups as possible in advance to be prepared.

But Google goes one step further: Bill Slawski points on his blog to a patent that deals with the topic under the title “Search entity transition matrix and applications of the transition matrix,” which not only involves assigning people, things, or places to entities but also forms and analyzes relationships between "search entities".

The patent abstract reads like this:

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using search entity transition probabilities. In some implementations, data identifying entities and transition probabilities between entities is stored in a computer-readable medium. Each transition probability represents a strength of a relationship between a pair of entities as they are related in search history data. In some implementations, an increase in popularity for a query is identified and a different query is identified as temporally related to the query. Scoring data for documents responsive to the different query is modified to favor newer documents. In other implementations, data identifying a first session as spam is received, and a spam score is calculated for either a second session of queries or a single query using transition probabilities. The second session (or single query) is identified as spam from the spam score.

But what exactly are "search entities"?

• The search query a user enters

• Documents delivered as a response to this search query

• Documents clicked by the user

• The dwell time on these results

• The search session in which the user conducts the search query (defined by a certain duration, by a specific number of queries, up to a certain time of inactivity, by the user's login, or by the similarity of the entered search queries, defined for example by similarity in spelling, abbreviations, or synonyms)

• The time at which the search query is conducted

• Advertisements delivered as a result of the search query

• The domain to which a document is assigned

• Link texts in a document

What does Google do with these entities?

A probability value is calculated based on the relationship between these different types of entities. These probabilities can have various impacts, such as:

• Identifying relationships between different entities and the strength of these relationships, for example, by analyzing the search history of the user.

• Using these relationships, values can be inherited from one entity to other related entities.

• Entities that do not have enough data on search history can be assigned values using related entities for which there is sufficient data.

• These relationships can also be used to more accurately rank search results and deliver vertical search results.

  

  
  

• Identifying search queries that are gaining in popularity and delivering preferred current results for them (We also remember the Freshness Update nearly two years ago.)

  

• Using the relationships between entities to suggest search queries to users.

• Relinking results for certain search queries based on links from related documents.

And what does this mean in practice? An example.

Google determines the strength of the relationship between a document (search result) and the entered search query using the entities and calculates the likelihood that the user will click on this result for a specific search query. An important criterion for the quality of a result could be the dwell time. The illustration from the patent gives a small insight into the analyzed entities:

Conclusion: Have Keywords Lost Their Importance?

Considering these developments, one might dismiss the importance of keywords and the practice of keyword research. However, that would be premature. While optimization is more complex today than it was years ago when using keywords in the title tag, headings, and content were among the most important measures. Although the focus is increasingly on semantic search, on concepts instead of keywords, the utility of keywords should not be overlooked.

Keyword research and analysis is still a legitimate practice that provides many helpful insights:

• It helps analyze what language the target audience speaks and ensures that the content is understood and perceived correctly by users.

• Keywords can help understand the intentions and needs of the target audience.

• Synonyms and related terms can be identified that still play a crucial role in quality content for a good writing style, readability, and understanding, even if Google search is fully based on entities.

• Keyword research helps uncover connections and accurately reflect the categorization of content on the website or online shop using suitable categories. Although a logical structure with a sensible navigation may eventually become unnecessary for Google, it will always be a must for usability reasons.

• Keyword research aids in a rough estimation of competition and the value of a keyword: How challenging will the optimization likely be, how costly is it to initially focus on AdWords instead of SEO? Which product areas should be expanded and enriched with content, for which business fields is it especially worthwhile to expand the editorial section and gain links?

However, it is always important to keep the context in mind. It is not always the best option to create landing pages based on keywords. Instead, it might become important in the future to analyze the target audience based on their contexts and to have relevant landing pages available for these groups and their respective situations.

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In any case, observing the developments of semantic search and learning how Google classifies and interprets terms is essential. Alongside Google Trends, which was mentioned earlier, the Suggest function and related search queries can also be helpful in this regard. It is also vital to keep an eye on how the Knowledge Graph evolves, and to understand the definitions Google makes for keywords and which terms help Google understand keyword relationships, avoiding incorrect assignments. Keywords, as a natural part of language use, are indeed still important here, but they do not replace structured data and – increasingly in the future – entities.

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