Knowledge Graph Construction

Learn how knowledge graphs are constructed.

Knowledge graph databases

Knowledge graphs are huge in size, so we need databases to store their information. These graph databases provide a way to store and retrieve entity and relationship information efficiently. They have built-in support for features such as RDF, ontologies, and semantics to perform advanced querying and reasoning tasks.

Some popular graph databases include ArangoDB, which is a free and open-source native graph database system that uses ArangoDB query language (AQL) to perform query tasks. It is a NoSQL database system, but its query language is similar to SQL and easy to learn.

Some databases offer access with commercial licenses, such as Neo4j, which uses Cypher query language to communicate with the graph database.

Graph construction

We can construct a knowledge graph from scratch using graph databases. Let's look at an example in which we create a knowledge graph using a graph database and Python. The Python database drivers can be used to access and manipulate the database using the Python programming language.

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