Conventional technology behind search engines such as Google ( ) uses algorithms…

Conventional technology behind search engines such as Google ( uses algorithms that look for pages that contain the search terms, rather than the meaning behind the terms. Google’s algorithms first check previous searches from other users using the same terms to determine which words in the query are most likely to be important, based on how often they have been used. Next, the software accesses a list of web pages that contain information related to these search terms. Finally, the search engine ranks the results and displays them to the user.

Newer technology from Google tries to uncover the meaning behind search terms. Google’s Knowledge Graph, which began when it bought a start-up called Metaweb in 2010, is a vast database that enables Google software to connect facts on people, places, and things to one another. The Knowledge Graph will enable Google’s future products to better understand the people who use them and the things they care about. Searchers will be able to use information gathered by the Knowledge Graph to answer their queries without having to navigate to other sites and assemble information themselves.

When Google began the Knowledge Graph project in 2010, Metaweb contained only 12 million entries. Today, the Knowledge Graph has more than 600 million entries containing more than 18 billion links.

Google designed the Knowledge Graph to examine queries in a much more sophisticated way and directly retrieve relevant information, although it still uses data from past searches to determine what might be the most relevant. The Knowledge Graph adds useful context and detail to the list of displayed links. Searching for certain people, places, or things produces a box of facts alongside the regular search results.

The Knowledge Graph has uses beyond a typical query. For example, Google has integrated Knowledge Graph into YouTube (, where it arranges videos by topic and suggests similar videos to the ones just watched. Within news articles, the Knowledge Graph can also connect and recommend news articles based on specific facts mentioned in the stories.

The Knowledge Graph is a database that represents Google’s knowledge of the world. A good analogy for the Knowledge Graph is maps. A map is based on a database of real-world information, outlining streets, rivers, countries, and other physical entities. Therefore, a map is a structure for the physical world.

The Knowledge Graph provides such a structure for the world of ideas and knowledge. For example, Google’s Knowledge Graph maps out information on foods, products, philosophy and history, famous people, and myriad others. The relationships among entities enable the Knowledge Graph to determine, for example, that these two people are married, that this place is located in this country, or that this person appears in this movie.

Google’s understanding of the information uncovered in the Knowledge Graph grows as it crawls and indexes documents, analyzing every item of information within the Knowledge Graph’s context. For example, if the document is about famous chefs, the Knowledge Graph knows it is about food and cooking.

Sources: Compiled from J. DeMers, “What Google’s Knowledge Graph Means for the Future of Search,” Forbes, October 28, 2014; J. Kahn, “Google Adds Video Game Data to Knowledge Graph in Search Results,” Techspot, October 24, 2014; T. Simonite, “How a Database of the World’s Knowledge Shapes Google’s Future,” MIT Technology Review, January 27, 2014; S. Perez, “Google’s Knowledge Graph Now Being Used to Enhance Search Results,” TechCrunch, January 22, 2014; A. Orlowski, “Google Stabs Wikipedia in the Front,” The Register, January 13, 2014; G. Kohs, “Google’s Knowledge Graph Boxes: Killing Wikipedia?”, Wikipediocracy, January 6, 2014.


  1. Provide two examples not contained in the case that businesses could make use of with Google’s Knowledge Graph.

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