Topicality in relation to search ranking algorithms has become of interest for SEO after a recent Google Search Off The Record podcast mentioned the existence of Core Topicality Systems as a part of the ranking algorithms, so it may be useful to think about what those systems could be and what it means for SEO.
Not much is known about what could be a part of those core topicality systems but it is possible to infer what those systems are. Google’s documentation for their commercial cloud search offers a definition of topicality that while it’s not in the context of their own search engine it still provides a useful idea of what Google might mean when it refers to Core Topicality Systems.
This is how that cloud documentation defines topicality:
“Topicality refers to the relevance of a search result to the original query terms.”
That’s a good explanation of the relationship of web pages to search queries in the context of search results. There’s no reason to make it more complicated than that.
How To Achieve Relevance?
A starting point for understanding what might be a component of Google’s Topicality Systems is to start with how search engines understand search queries and represent topics in web page documents.
- Understanding Search Queries
- Understanding Topics
Understanding Search Queries
Understanding what users mean can be said to be about understanding the topic a user is interested in. There’s a taxonomic quality to how people search in that a search engine user might use an ambiguous query when they really mean something more specific.
The first AI system Google deployed was RankBrain, which was deployed to better understand the concepts inherent in search queries. The word concept is broader than the word topic because concepts are abstract representations. A system that understands concepts in search queries can then help the search engine return relevant results on the correct topic.
Google explained the job of RankBrain like this: