What is the Google Knowledge Graph?

What is the Google Knowledge Graph?

The Google Knowledge Graph is a system that Google launched in May 2012 that understands facts about people, places and things and how these entities are all connected. 

Using Schema Markup to Adapt to the Semantic Evolution of Search

Since the introduction of the Google Knowledge graph in 2012, it is becoming increasingly apparent that the semantic web or web data that can be understood by search engines is the future of search.

Google and other search engines are shifting from the previous model of keyword strings determining search rank to understanding semantic language principles.

They are becoming better at evaluating intent and contextual meaning of user search phrases and incorporating location, synonyms, current trends and other natural language patterns or conversational language to produce more relevant search results. SEO strategy will have to take numerous steps to adapt to these rapidly advancing changes.

How does the Google Knowledge Graph and Semantic search all work together?

Before the Knowledge Graph, search was all about matching keywords to queries. However, Google has shifted focus to what they call “things not strings” or entities and their relationships between each other rather than keyword strings.

The integration of the Hummingbird algorithm update and growth of the Knowledge Graph has resulted in Google and other search engines becoming more efficient at conversational search or understanding natural language.

The Google Knowledge Graph is an entity graph mapped out with things and the relationship between them.

Entities are people, places or things. Previous reliance on keywords to identify patterns in order to determine what content was about meant that it was difficult for search engines to attach meaning to search results.

Enabling semantic search requires labelling information as unique entities with structured data, such as schema markup.

Unique entities allow search engines to disambiguate search queries that consist of natural language patterns to produce more relevant results – meaning that search engines can differentiate between similar words with different meanings.

eg When someone searches for “Taj Mahal” a search engine can now understand if a user is searching for Taj Mahal the monument or Taj Mahal the singer.

These changes mean that simply focusing on keywords will not be enough to create search engine optimised web pages