Today, when you enter a search term into Google, the company kicks off two separate but parallel searches. One runs against the traditional keyword-based Web index, bringing back matches that are ranked by statistical relevance—the familiar “ten blue links.” The other search runs against a much newer database of named entities and relationships.
This second brain is called the Knowledge Graph.
It’s based on Freebase. It’s a collaborative database—technically, a semantic graph—that grows through the contributions of volunteers, who carefully specify the properties of each new entity and how it fits into existing knowledge categories. (For example, Freebase knows that Jupiter is an entity of type Planet, that it has properties such as a mean radius of 69,911 km, and that it is the fictional setting of two Arthur C. Clarke novels.) While Freebase now hosted by Google, it’s still open to submissions from anyone, and the information in it can be freely reused under a Creative Commons license.
Metaweb had to break away from the classic relational-database model, in which data is stored in orderly tables of rows and columns, and build its own proprietary graph database. In a semantic graph, there are no rows and columns, only “nodes” and “edges,” that is, entities and relationships between them. Because it’s impossible to specify in advance what set of properties and relationships you might want to assign to a real-world entity (what’s known in database lingo as the “schema”), graph databases are far better than relational databases for representing practical knowledge.
Fascinating stuff on the future of Google Search.