Knowledge graphs are essential to many AI and machine learning applications.
You can use a knowledge graph to give context and ground an LLM, giving it access to structured data beyond its initial training data.
Knowledge graphs are a specific implementation of a Graph Database, where information is captured and integrated from many different sources, representing the inherent knowledge of a particular domain.
They provide a structured way to represent entities, their attributes, and their relationships, allowing for a comprehensive and interconnected understanding of the information within that domain.
What are Knowledge Graphs?
Knowledge graphs break down sources of information and integrate them, allowing you to see the relationships between the data.
You can tailor knowledge graphs for semantic search, data retrieval, and reasoning.
What are Knowledge Graphs?
You may not be familiar with the term knowledge graph, but you have probably used one.
Search engines typically use knowledge graphs to provide information about people, places, and things.
The following knowledge graph could represent Neo4j.
Integrating data
This integration from diverse sources gives knowledge graphs a more holistic view and facilitates complex queries, analytics, and insights.
