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6-Minute Guide to Knowledge Graphs and Neo4j
Nodes, Relationships, Properties and Queries in Knowledge Graphs
What is a Knowledge Graph?
In the analytics domain, relational databases are among the most commonly encountered database types. They store data in a tabular format, which enables efficient data retrieval and analysis. Graph databases go beyond the rigid tabular structure with fixed data schemas, using nodes and edges to represent the connections between data points. There are several representations of graph databases, and knowledge graph (or the underlying property graph model) is one of the most popular types, with several variations e.g. objected path property graph, hyper-vertex property graph. A knowledge graph has three fundamental elements — node, edge and property, and we will discuss them in more details along with the implementation in Neo4j.
Why Do We Need Knowledge Graphs?
Knowledge graphs are efficient for handling big data with complicated relationships between data points and the design of graph databases typically results in much faster query time compared to a relational database. They are also more flexible due to their schema-less nature, meaning that it does not force all data points to have the same fields.
