Knowledge graphs are a huge collection of facts. They allow the storage of information about sports, TV shows, places, people, and so on. These types of graphs are multi-relational.

Differences between knowledge graphs and other graphs

Knowledge graphs are just like any other graph, but they're huge in size. They can consist of millions of nodes and edges. Normally, the term knowledge graph is used a bit vaguely. Broadly speaking, there's no fixed definition for a knowledge graph, and we can call any sizeable graph holding some knowledge or important information a knowledge graph.

Some very popular knowledge graphs include the following:

  • Google Knowledge Graph with 500 billion facts and 5 billion entities.

  • Amazon Product Graph.

  • Open-source knowledge graphs like DBpedia, and Wikidata.

So, what are the building blocks of a knowledge graph? Let's understand how these knowledge graphs are constructed by taking a look at the elements that make up the knowledge graph.


Knowledge graphs are usually represented as directed graphs, so each entity of this graph can be termed as a triple. As the name suggests, a triple is a tuple made of three elements: the source node, the relation, and the target node.

Let's check the nomenclature of a triple. The elements of triples can be referred to using different names, such as:

  • (s,p,o): Subject, predicate, object

  • (h,r,t): Head, relation, tail

  • (s,r,t): Source, relation, target

Take note of the following illustration:

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