Knowledge graphs, that are fueled by machine learning, utilize natural language processing (NLP) to construct a comprehensive view of nodes, edges, and labels through a process called semantic enrichment.

Learn the basic terminology, concepts, and applications of knowledge graphs, a directed labeled graph representation of domain-specific information. See how knowledge graphs are used for organizing information over the internet and data integration in enterprises.

Learn how to create and use a knowledge graph, an organized representation of real-world entities and their relationships. A knowledge graph is stored in a graph database, such as Neo4j, and can be used for various applications, such as search, discovery, and AI.

Learn about knowledge graphs, which are graph-based data models and query languages for exploiting diverse, dynamic, large-scale collections of data. This paper covers the roles of schema, identity, and context, the techniques for representation and extraction, the methods for creation and quality assessment, and the applications and future directions of knowledge graphs.

- What is a Knowledge Graph (KG)? - Why KG? - How to use KG? KG in practice. - Open source KGs. - Creating custom KG. - KG Ontology. - Hosting KG (database) - Query facts from KG. Introduction. In this section, we will introduce KG by asking some simple but intuitive questions about KG.

In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future ...

Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources.

A knowledge graph is a structured representation of information that connects entities through meaningful relationships. Entities can be any concept, idea, event, or object, while relationships are edges that connect these entities meaningfully.

Learn the steps to create a knowledge graph that represents concepts, entities, and their connections in a graph database. This guide covers data integration, ontology modeling, graph population, validation, querying, and visualization.