Knowledge Graph
A structured representation of entities and their relationships that enables machines to understand connections and reason about domain knowledge.
In Depth
A knowledge graph is a structured data model that represents real-world entities, their properties, and the relationships between them in a graph format. Entities are represented as nodes, relationships as edges, and properties as attributes attached to either nodes or edges. This representation enables machines to traverse connections, answer complex queries, and reason about relationships in ways that are difficult with traditional tabular or document-based data storage.
Knowledge graphs have found renewed importance in the era of large language models. GraphRAG combines traditional RAG with knowledge graph traversal, enabling AI systems to answer multi-hop questions that require following chains of relationships across entities. For example, answering "What regulations apply to the subsidiaries of companies in our portfolio?" requires traversing portfolio holdings, corporate structures, jurisdictional mappings, and regulatory frameworks, which is naturally represented as graph traversal.
Construction of knowledge graphs involves entity extraction (identifying named entities in text), relationship extraction (determining how entities relate), entity resolution (merging duplicate references to the same entity), and schema definition (establishing the types of entities and relationships in the domain). Modern approaches use LLMs to automate much of this extraction process, dramatically reducing the manual effort traditionally required for knowledge graph construction.
Enterprise applications of knowledge graphs include organizational knowledge management, supply chain mapping, regulatory compliance tracking, fraud detection through relationship analysis, recommendation systems, and scientific research discovery. When integrated with AI systems, knowledge graphs provide structured, verifiable context that complements the pattern-matching capabilities of neural networks. Key platforms include Neo4j, Amazon Neptune, and open-source options like Apache Jena, with the choice depending on scale requirements, query patterns, and integration needs.
Related Terms
Semantic Search
Search technology that understands the meaning and intent behind queries rather than matching keywords, using vector embeddings for relevance.
RAG (Retrieval-Augmented Generation)
A technique that enhances large language model outputs by retrieving relevant documents from an external knowledge base before generating a response.
Natural Language Processing (NLP)
The field of AI focused on enabling computers to understand, interpret, generate, and interact with human language in useful ways.
Neural Network
A computing system inspired by biological neural networks, consisting of interconnected layers of nodes that learn patterns from data through training.
Embeddings
Dense numerical vector representations that capture the semantic meaning of text, images, or other data in a high-dimensional space.
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