Google Hummingbird

What is Google Hummingbird?

Google Hummingbird – The industry nickname for one of the first major overhauls to the main Google search algorithm. In contrast to algorithm updates like Panda or Penguin, Hummingbird was intended to completely update the way Google interpreted user search queries. Previous to this update, Google results were mostly provided based on specific keyword matching within the user query. Now, a search for “Cheapest way to build birdhouse without using wood” will show results directly related to that query. Previously, users might see results that included wood as a building material.

Google Hummingbird and Semantic Search

At the heart of the Hummingbird lies the all-important concept of semantics or meaning. Even the fanciest computers are still stupid. This is because although it’s easy for humans to distinguish between two different yet similar concepts (by virtue of context), computers can’t do this unless they’re explicitly told. Stupid computers.

Semantic search is the concept of improving search results by focusing on user intent and how the subject of a search relates to other information in a wider sense or its contextual relevance. Essentially, the semantic search focuses on determining what a user really means, rather than a string of keywords, and then serving relevant results.

For example, if a user performs a search for the term “weather”, it’s much more likely that they are looking for a forecast for their area, not an explanation of the science or history of meteorology.

So, in this example:

  • “Weather” is the subject of the search
  • The desire for a local forecast is the user’s intent
  • The difference between a weather forecast and an explanation of meteorological concepts is the context

Of course, Google’s algorithm cannot be sure of what I want, so just to be safe, it provides me with a range of results. Google serves up a local forecast (even though this search was performed in an Incognito window, it still tracks my location), a link to the Weather Channel, a Wikipedia page for the term “weather” and some other information. Still, the prominence of the local forecast data in the Knowledge Graph speaks volumes about Google’s confidence in its results.


The Semantic Web

So, if the semantic search is the quest to provide relevant results based on user intent and context, then the semantic web must be all sites doing something like this, right? Wrong. Although similar in name, semantic search and the semantic web are vastly different.

The semantic web is a largely unrealized vision of an internet-based on common standards. Imagine if every website featured structured data such as schema and that new technologies were developed to read, retrieve and publish data based on common data models. The result, a semantic web, would be an internet in which machines could perform much of the heavy lifting associated with search by truly understanding and responding to user queries, rather than the comparatively fractured web we have right now.

Google Hummingbird and the Knowledge Graph

When researching this post, I wanted to know how many Google searches are performed every day. Prior to the rollout of Google Hummingbird, I would have been presented with a SERP containing links to numerous pages, through which I could have probably learned the answer.

Google realized that this was a slow and often irritating process, even for users who were presented with relevant results. Google Hummingbird makes search quicker, easier and more intuitive.


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(See also: Google Algorithm, Google Panda, Google Penguin)

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