What Is a Knowledge Graph?
Key Takeaway: A knowledge graph is a structured representation of entities and their relationships, designed to encode domain knowledge in a machine-readable form. It enables systems to store and retrieve meaning across connected concepts.
As data systems scaled, simple relational databases became insufficient for representing complex relationships. Knowledge graphs solve this by allowing semantic querying, entity relationship mapping, cross-domain reasoning, and structured discovery.
Google popularized the concept in 2012 with its Knowledge Graph, which connects billions of facts to improve search results. Since then, knowledge graph AI applications have expanded across industries. Gartner has identified knowledge graphs as a core enabling technology for decision intelligence platforms.
Source: Gartner, Market Guide for Decision Intelligence Platforms, July 2024.
Entities are defined objects within a domain. A person, a company, a product, a concept.
Relationships are explicitly defined connections. "Company X employs Person Y." "Product A competes with Product B." Each relationship is typed and directional.
Ontological rules govern how entities relate. They define what types of relationships are valid between what types of entities. This schema prevents the graph from becoming an unstructured mess.
Query framework allows structured retrieval. You can ask "What companies are connected to Person Y through acquisition?" and get a precise answer.
A knowledge graph stores and connects information. It tells you how things relate. That's its strength.
It does not, by itself, recommend actions. It does not evaluate trade-offs. It does not tell you which of your decisions are at risk. A knowledge graph tells you how things connect. Decision AI tells you what to do about those connections.
Knowledge graphs serve as a foundation for AI systems that require relational awareness. They provide the structural layer that contextual reasoning and decision intelligence platforms build on top of.
DecisionX builds on knowledge graph architecture to maintain structured relationships between business entities, then adds reasoning, foresight, and action recommendation on top.
Want to know the difference: Ontology vs Knowledge Graph
DecisionX puts Decision AI into practice by continuously monitoring signals, structuring context, reasoning across hypotheses, and surfacing the next best action within a single system.
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