New standard for semantic search

In June 2011, three main search engines (Google, Bing and Yahoo) created a new standard for semantic search: . The semantic search dictionary was created: adding its elements we can define if a man is a moviedirector , the length of a video or how many comments an item received. . Adding these tags won’t make you climb position, but you’ll have much more: your results will become nicer than simple links since they will be transformed in “rich snippets. Now Microsoft want to make Bing’s Satori knowledge repository equal to Google’s Knowledge Graph. Microsoft recently announced improvements to Satori (a Zen Buddhist term for enlightenment) that will bring more data front-and-center for searches of historical people and events, countries, and scientific data. Tying together multiple profiles and networks to serve up information about a person, a place or a thing creates challenges for search engines. It’s like stringing or graphing together an underlying net below the surface of the Web to connect all things throughout the world. It is based on the relationship between entities or links. Google created the Knowledge Graph, which will become the backbone for artificial intelligence built into products and services, . Now Microsoft Bing will give it a try. The technology becomes the system that is smart enough to connect the dots and search on the someone’s behalf — creating a push model rather than pull that is helping people do things rather than find them.  Bing introduced Snapshot, which enables people to get answers at a glance in the center column of the search results page. The feature started with movies, restaurants and hotels. The underlying technology, Satori — which means understanding in Japanese — will connect “billions of entities and relationships” in time to serve up more useful information about the physical world. It taught Bing how to understand the Web.The Satori graph-based repository, derived from Facebook also uses the same process in Search Graph — but turned metadata for nodes into social interactions, so it can tell you things like close-by entities or things people liked or shared. “The heart of Graph Search is called Unicorn, an in-memory database based on an inverted index. Inverted indexes have been used by a number of search technologies that associate words, fragments of words, or types of relationships with attributes of entities within Facebook’s graph database. Those associations, called ‘marks’ by Facebook, are stored in a Unicorn in-memory index, pointing to the Facebook numerical identifier for related entities,” according to ArsTechnica.

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