This paper provides a comprehensive introduction to knowledge graphs, which have gained significant attention from both industry and academia for managing and extracting value from diverse, dynamic, large-scale data. It discusses various graph-based data models and query languages, the roles of schema, identity, and context, and methods for creating, enriching, and publishing knowledge graphs. It also covers prominent open and enterprise knowledge graphs, their applications, and future research directions. Knowledge graphs are graph-based data models that represent data using nodes and edges, where nodes represent entities and edges represent relations between them. They are used to integrate, manage, and extract value from diverse data sources. The paper discusses different graph data models, including directed edge-labelled graphs, heterogeneous graphs, property graphs, and graph datasets. It also covers query languages for graph data, such as SPARQL and Cypher, and their use in retrieving and analyzing graph data. The paper also discusses schema, identity, and context in knowledge graphs, explaining how they help in structuring and interpreting data. It covers semantic schemas, which define the meaning of terms and relations, and validating schemas, which ensure data consistency and completeness. The paper also discusses emergent schemas, which are automatically extracted from data graphs to capture latent structures. The paper concludes with a discussion of practical applications of knowledge graphs, including open knowledge graphs such as DBpedia and enterprise knowledge graphs used in industries like web search, commerce, and finance. It also outlines future research directions for knowledge graphs, including improving query languages, enhancing schema definitions, and exploring new applications.This paper provides a comprehensive introduction to knowledge graphs, which have gained significant attention from both industry and academia for managing and extracting value from diverse, dynamic, large-scale data. It discusses various graph-based data models and query languages, the roles of schema, identity, and context, and methods for creating, enriching, and publishing knowledge graphs. It also covers prominent open and enterprise knowledge graphs, their applications, and future research directions. Knowledge graphs are graph-based data models that represent data using nodes and edges, where nodes represent entities and edges represent relations between them. They are used to integrate, manage, and extract value from diverse data sources. The paper discusses different graph data models, including directed edge-labelled graphs, heterogeneous graphs, property graphs, and graph datasets. It also covers query languages for graph data, such as SPARQL and Cypher, and their use in retrieving and analyzing graph data. The paper also discusses schema, identity, and context in knowledge graphs, explaining how they help in structuring and interpreting data. It covers semantic schemas, which define the meaning of terms and relations, and validating schemas, which ensure data consistency and completeness. The paper also discusses emergent schemas, which are automatically extracted from data graphs to capture latent structures. The paper concludes with a discussion of practical applications of knowledge graphs, including open knowledge graphs such as DBpedia and enterprise knowledge graphs used in industries like web search, commerce, and finance. It also outlines future research directions for knowledge graphs, including improving query languages, enhancing schema definitions, and exploring new applications.