tudásgrafikonokat

Tudásgrafikonok, or knowledge graphs, are structured representations of information that depict relationships between real-world entities. They consist of nodes, representing entities like people, places, or concepts, and edges, which define the connections and relationships between these entities. For example, a knowledge graph might have a node for "Budapest" and another for "Hungary," with an edge labeled "is capital of" connecting them.

The primary purpose of knowledge graphs is to organize and interlink data, making it more accessible and understandable for both humans and machines. They enable advanced data analysis, facilitate semantic search, and power recommendation systems. By capturing the context and relationships within data, knowledge graphs go beyond simple databases, providing a richer, more interconnected view of information.

Knowledge graphs are constructed through various methods, including automated extraction from unstructured text, manual curation, and integration of structured datasets. They are increasingly used in diverse fields such as artificial intelligence, natural language processing, e-commerce, and scientific research. The underlying structure allows for reasoning and inference, meaning that new facts and relationships can be discovered from the existing graph.