Knowledge Graphs in Detail

AI-powered structured representations of knowledge.

Knowledge graphs utilizing artificial intelligence represent knowledge as structured graphs with entities, relationships and attributes. From automated construction to querying, from embeddings to reasoning – AI can significantly improve the representation and utilization of knowledge.

AI Role: Automated Discovery

Result: Structured Relationships.

Examples: Name, date, value

live demo · related simulation● LIVE

AI Role: Natural Language Queries

Result: Easy Access.

FAQ: Questions and Answers

Frequently asked questions

What is the role of graph embeddings in knowledge graphs?

Graph embeddings (Node2Vec, GraphSAGE) represent entities and relationships within a vector space. This enables finding similar entities, making recommendations, and performing semantic search.

How do you query a knowledge graph?

You can use query languages like SPARQL or Cypher to perform structured queries. Alternatively, AI can translate natural language questions into graph queries, making the process more intuitive.

▶ Try it live

Everything above runs in your browser — open Force-Directed Graph and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Force-Directed Graph simulation