Knowledge Graphs

- Overview 

Knowledge graphs (KGs), also known as semantic networks, represent a network of real-world entities (such as objects, events, situations, or concepts) and illustrate the relationships between them. This information is often stored in graph databases and visualized as graph structures, giving rise to the term knowledge graph.

KG is used in data science and AI to:

• Integrate data from multiple sources
• Add context to artificial intelligence technologies like machine learning
• Produce human-readable explanations
• Providing smart systems for scientists and engineers
• Ground-level generative artificial intelligence for question answering
• Represents information extracted using natural language processing and computer vision

KG is usually stored in a graph database. They can be implemented in different ways, including:

• Virtualization: KG is a smart index of externally stored data.
• Materialization: External data is fully replicated in the graph platform.
• Hybrid: A combination of virtualization and materialization approaches.

Google’s Knowledge Graph is a repository of billions of facts about people, places, and things. It is used to answer factual questions such as "How tall is the Eiffel Tower?". 

Please refer to the following for more information:

• Wikipedia: Knowledge Graph

[More to come ...]