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The Good, Bad and Ugly about Google Hummingbird

Hummingbird drinking water droplet

Named for its accuracy and speed, Google’s latest algorithm “Hummingbird” redefines organic search on the internet. Its two key features – conversational  search and semantic search differentiate it from its predecessors making  it the search engine of choice for hands free mobile devices of the present and future.

Although reintroduced in its new avatar, conversational search (or searching by asking Google a question as you would in a conversation (as opposed to typing your search phrase into the search box), really began in 2011 as voice search. Widely adopted on mobile devices for its obvious convenience, it led to Google adopting semantic search as Hummingbird’s search logic in 2013.

Semantic search is the ability to search by understanding  language or the meaning of spoken words based on context and the searcher’s intent. In semantic search, keywords alone are less important. They become symbols for the meaning they convey. In choosing conversational search, Google somewhat pre-committed to semantic search.

Hummingbird searches differently from its predecessors that used keyword matching to find results regardless of context or meaning. When we ask  Google a question, Hummingbird tries to first  figure out what we mean and what we might be looking  for rather than simply “chase the phrase”. It then finds web pages whose content conceptually matches the meaning of our question. This increases the relevance of its search results.

In order to help the Hummingbird figure out meaning, Google has compiled a proprietary database called the “knowledge graph”. The knowledge graph contains “entities” which are keywords, synonyms, and their variations based on years of Google’s own search history,  and drawn from various other external sources such as the CIA WorldFactbookFreebase and Wikipedia.  It shows the relationships between millions of  entities and how they are interrelated. It is a dynamic tool that evolves and improves as it learns from the web,  drawing on its “collective consciousness”, so to speak.

Hummingbird accesses the knowledge graph to understand  what our search queries mean and finds conceptually and contextually matching results. If you were to ask Google a question such as “Where can I find a Ben and Jerry’s”,  Hummingbird would figure out that “Ben and Jerry’s” is the name of a business that sells ice cream. It would then speak to you, providing you with a list of all the Ben and Jerry’s locations near you. This is a huge improvement over Google’s earlier search that would have shown you matches for  “Ben”,  “Jerry” and “Ben and Jerry’s”.

Now, if you were to ask  Google, “Are they open now?”  Hummingbird would figure based on your previous question that “they” refers to Ben and Jerry’s. It would then show you the business hours for the locations it had pulled up . Pretty cool, right?  Hummingbird makes search easy by making a contextual connection between your first question and the next. It is Google’s best attempt yet to design an intelligent search that flows like a conversation.

Search engines as a rule want us to spend as little time searching  on them as possible. To make search easy and quick , Google sometimes displays short answers containing just the bare facts. These answers or “knowledge cards” are based on the information contained in Google’s knowledge graph, and are displayed right on top of the search results page, eliminating the need to scroll down or click on any of the links displayed further down the page.

Were  knowledge cards to become the norm in the future,  it would help Google improve search on the one hand, but might reduce organic traffic to websites and click-through rates for paid search.

Will information cards become the nemesis of SEO and paid search in the future? Would Google be willing to sacrifice a huge chunk of its ad revenue in order to make search quicker? What are your thoughts? Let’s hear from you now.

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About the Author

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I started my career communications as a non-engineer in an engineering-dominated, metallurgical firm. My responsibility was to create easy to understand, relatable communication for the company's corporate audience which did not always comprise engineers. The assignments I've worked on throughout my career, have reinforced the lessons I learnt at my first job - to view communication from the audience's perspective, free from the inherent biases and assumptions of business owners and line managers. I add value by creating communication that is relatable and therefore engaging for the audience who does not necessarily share the perspective, or professional background of the message creator. I bring a unique blend of creativity, analysis, and intuition to my work, combining knowledge and experience with business understanding and a sense of aesthetics.

2 Comments

  1. Sudha

    Great observation! We really want to be understood … improving search algorithms may be the basis for future translation algorithms – especially if it can be used on actual voice conversations. Then – any language can get an accurate response. Wow – sounds too good to be true.

    Like

  2. Abhay

    Quite interesting . . Conversational search and semantic search seem like steps towards strong AI . .

    I guess Google’s work on search will help its Ad revenues in the long run . .

    Like

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