When Larry Page and Sergey Brin founded Google in 1994, their vision was to create a single universal digital library that would allow people to find information with a few simple keystrokes. Over time, the biggest challenge that Google search technology has encountered is how to deal with the low-quality, spammy Web pages that plague the Internet. However, Google has embraced this challenge and is determined to deliver a solution.
Recent history shows an increased acceleration of Google’s pursuit to return the highest quality results of any search engine. In 2011, Google released its Panda update which successfully removed a significant number of lower-quality Web pages from its search results. It appears that in 2012, Google Semantic Search is the search engine’s next foray into algorithmic refinement.
What Is Google Semantic Search?
Semantic Search is Google’s attempt at refining and enhancing search results to reflect how humans understand the world.
- In its current environment, Google search results are primarily influenced by the relevance of keywords to appropriate landing pages.
- In its future state (semantic), a searcher’s intent married to associated linguistic patterns will be just as important as the keyword queried.
Ultimately, semantic search adds another layer of intelligence to an algorithm focused on returning the highest quality search results. While there are semantic search engines in the market, such as WolframAlpha, Google is betting that the combination of social, personalized and semantic search will give them an edge over the competition.
How Will Google Semantic Search Change The Game?
While much of the buzz on the Web would lead you to believe that semantic search is new to Google, the company has actually been infusing semantic elements into its search results for years. As an example of how Google has adopted semantic search into its current algorithm, consider the query “army boot camp”:
While the above goarmy.com page does not use the term “boot camp” within the page’s content, Google still returns the synonymic page, “Basic Combat Training.” Ultimately, Google has identified a semantic relationship between “army boot camp” and the overall theme of Basic Combat Training.
Nevertheless, the level of semantic intelligence that Google will integrate into its ranking algorithm in the near future remains uncertain. What we do know is that similar to Google’s Panda Update, semantic search will be iterative in nature and become a key feature of Google’s search results.
How does Google create semantic relationships?
Because Google is highly protective of its algorithms, pointing to a specific factor within Google’s secret sauce is not possible. However, several key business decisions:
- In 2003, Google acquired Applied Semantics, whose technology gave power to Google’s Adsense product. 
- In 2009, in an attempt to compete with semantic search engine WolframAlpha, Google launched a Labs project called Google Squared. Similar to WolframAlpha’s semantic engine, Google Squared attempted to provide direct answers to a question queried on the search results page, without asking the user to click a link. Google Squared was suspended in 2011 and we at those who have studied semantic search believe that much of the insight gained in its short life was integrated into Google’s current algorithm. 
- In 2010, Google acquired Metaweb, a company that maintained an open database of entities in the world. By working together, Google aimed to improve search and make the web richer and more meaningful for everyone.
The acquisition of Metaweb is one of the most telling moves that Google has made in the pursuit of semantic search expertise. Metaweb developed an open-source database called Freebase which functions as a user-generated “wiki” that allows users to quickly find common information more quickly and effectively. In Freebase’s own words, it is “an entity graph of people, places and things, built by a community that loves open data.” 
Ultimately, if Google is looking to integrate semantic relevance into its algorithm, then it has to gain insight into how people look at the world and the Web itself. Freebase is a tool that provides Google with a more intimate understanding of these relationships. Furthermore, Google’s goal in its attempt to socialize (Google +) and personalize (Personalized and Local Search) its search results is not just to add an additional layer of sophistication to its product, but to expand its data collection to better understand how entities and information relate to each other.
Why the Move to Semantic Search?
In order to maintain its dominant search engine market share, Google knows that it must improve the quality of its search results. For years, irrelevant and spammy search Web pages have plagued search results (this is true about most of the major search engines). Along with its Panda update, Google’s shift to a semantic based search engine gives it an opportunity to improve its product and, as a result, increase profits. Furthermore, with the evolution of the Web and competitor search engines, it’s important for Google to adapt to a more sophisticated and demanding customer, the searcher.
What Semantic Search Means for Your Paid, Organic Search and Content Strategy
One thing search engine marketers know for sure is that search engine algorithms, processes and features will continue to evolve as the needs and expectations of its users do. While we know algorithms and processes change over time, there is one overarching philosophy that has remained consistent: build for users, not for search engines. 
If we as search marketers are going to create high quality content for users, understanding consumer search behavior, linguistic patterns, linguistic relationships and analytics data is imperative to help guide the tone and language that is used. This added layer of semantic relevance means that we must diversify our toolsets, processes, and strategies to uncover semantic insights. At MRM-NY, our search team has developed a proprietary semantic search optimization construct that we will be testing over the next few months and I will keep my blog updated with any and all results we find.
To learn more about how MRM is approaching the semantic Web, please contact us at t: 646-865-6230 / e: email@example.com