A knowledge graph is defined as a semantic data model that organizes information into interconnected entities, represented as nodes, and their relationships, represented as edges 1. This structured network is designed to be understandable by both humans and machines 1. The modern incarnation of the term gained prominence following Google's announcement of its Knowledge Graph in 2012 2. Fundamentally, it serves as a semantic model that captures business meaning and relationships, defining what data represents and how concepts interrelate 1. It functions as a graph of data intended to accumulate and convey "explicit knowledge" of the real world 2.
The core architecture of a knowledge graph comprises several key components that facilitate its ability to structure and interpret data effectively:
| Component | Description |
|---|---|
| Nodes | Represent "things" or entities in the data ecosystem, such as customers, products, transactions, or locations 1. These can be real-world objects, concepts, or data points 3, and each node typically includes attributes describing its properties 1. |
| Edges | Define the connections or relationships between entities 1. Relationships link two nodes, indicating how entities are related 4; for instance, a customer node might be linked to a product node by an "purchased" edge, providing crucial contextual information 1. A common representation of these relationships is through "triples" in the form of (subject, predicate, object) 5. |
| Labels and Properties | Labels categorize entities (e.g., "Customer," "Product"), while properties store specific attributes (e.g., "name," "price," "timestamp"), adding semantic richness to both nodes and relationships within the graph 1. |
| Ontologies | Provide the conceptual framework that defines the meaning of entities and relationships within a specific context, establishing rules and constraints for data connections 1. An ontology is a formal framework that defines the structure, rules, and vocabulary of the knowledge graph, ensuring consistency and semantic understanding 3. |
Unlike traditional databases with rigid, tabular structures, knowledge graphs focus on capturing the nuanced connections between different pieces of information, creating a unified, intelligent framework where relationships are as crucial as the data itself 1. This emphasis on connections and context allows knowledge graphs to move beyond treating information merely as text or simple data points, enabling a context-driven understanding that transforms "strings to things" 4.
The fundamental purpose of a knowledge graph lies in its powerful approach to organizing information, making complex data relationships accessible and actionable. By capturing and leveraging these inherent connections, knowledge graphs can unify siloed data sources to create a coherent view of information 1. This capability not only uncovers hidden insights missed by conventional data approaches but also significantly improves the performance of AI systems, including Large Language Models (LLMs), by providing structured context and reducing phenomena like hallucinations 1. Consequently, knowledge graphs serve as critical context engines for AI, enhance search accuracy and relevance, and facilitate smarter decision-making across various applications 1.