A knowledge graph is a structured representation of real-world entities and the relationships between them, stored as a network of interconnected nodes and edges. Unlike traditional relational databases that organize data into rigid tables, knowledge graphs capture semantic meaning by modeling information as subject-predicate-object triples, making it possible for machines to understand context and infer new facts from existing data.
Knowledge graphs power many of the intelligent features users interact with daily. Google's Knowledge Graph, for instance, drives the information panels that appear alongside search results, while similar technologies underpin recommendation engines, virtual assistants, and fraud detection systems. In enterprise settings, knowledge graphs unify siloed data sources into a coherent semantic layer, enabling more sophisticated querying, analytics, and knowledge discovery.
The construction of a knowledge graph typically involves entity extraction, relationship mapping, ontology design, and continuous enrichment from structured and unstructured data sources. Technologies such as RDF (Resource Description Framework), OWL (Web Ontology Language), and graph databases like Neo4j and Amazon Neptune provide the infrastructure for building and querying these graphs. With the rise of AI and large language models, knowledge graphs have gained renewed importance as a way to ground machine learning outputs in verified, structured knowledge, reducing hallucinations and improving factual accuracy.