Google has consistently pursued this goal since purchasing the semantic database Freebase, introducing the Knowledge Graph as a semantic database in 2012 and introducing the latest ranking algorithm basis Hummingbird in 2013. Accessing the world's knowledge requires an index that is structured as a graph. While Google accessed an index similar to a tabular database before 2012, in which information was stored like a directory, a graph index can capture and map relationships between information and entities.
Here is a list of the most important innovations that Google has introduced since 2010 on the way to becoming a semantic search engine:
2010: Google buys Freebase , a semantic database created by the company Metaweb with structured, machine-readable data on entities. The first version of the Knowledge Graph was fed by data from Freebase. In 2014, Freebase was transferred to the Wikidata project. Of the original 10 million records from Freebase, only a portion was transferred. For example, my own saudi arabia cell phone number list record on the entity " olaf kopp ", which I created in Freebase in 2012, was not transferred to Wikidata. I manually updated it there. Nevertheless, my data from the former Freebase database is still displayed in the form of a knowledge panel and expanded with additional information.
2012: Google introduces the Knowledge Graph in the form of Knowledge Panels and Knowledge Cards into search. A Knowledge Graph is a knowledge database in which information is structured in such a way that knowledge is created from the information. In a Knowledge Graph, entities (nodes) are related to one another via edges, assigned attributes and placed in thematic context or ontologies . More on this further down in this article or here >>> Google Knowledge Graph simply explained
2013: Google introduces the Hummingbird update as a new generation of ranking algorithms. The introduction of Hummingbird on Google's 15th birthday in 2013 was the final launch of semantic search for Google. Google itself described this algorithm update as the most significant since the Caffeine update in 2010. At the time of its introduction, it affected around 90% of all search queries and was a real algorithm update compared to Caffeine. It is intended to help interpret more complex search queries better and to better identify the actual search intent or question behind a search query and to offer suitable documents. The actual intent behind the content should also be better matched with the search query at the document level . More information >>> What is Google Hummingbird?
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