A common question we get is: How do Data Object Graphs (DOGs) differ from traditional Knowledge Graphs (KGs)? While both leverage graph structures, their core purpose, structure, and functionality are fundamentally different.
In short:
📌 Knowledge Graphs represent what things ARE.
📌 Data Object Graphs represent what things DO.
This distinction is crucial because DOGs go beyond knowledge representation—they execute business processes dynamically.
Breaking It Down: DOGs vs. KGs
1️⃣ Data Object Graphs (DOGs): Execution and Business Process Modeling
DOGs aren’t just about connecting data—they are about executing business processes in real time.
✅ Process-Oriented: DOGs are direct representations of business workflows and can be executed as part of a system.
✅ Executable Nodes: Unlike passive KGs, DOGs contain executable components, such as ML models, APIs, SQL virtualized engines, or decision models.
✅ 1:1 Mapping to Business Process: DOGs mirror how a business actually operates, making them a live implementation rather than an abstract representation.
✅ Agility & Adaptability: Because they map real-world operations, DOGs enable rapid change managementwithout requiring deep, complex rework of ontology models.
✅ Simulating Business Change: DOGs can test, simulate, and validate new business processes in real-time, leveraging real data and AI-driven execution.
2️⃣ Knowledge Graphs (KGs): Static Knowledge Representation
KGs, on the other hand, focus on semantic meaning and relationships between concepts.
✅ Concept-Oriented: KGs define relationships between entities (e.g., "Paris is a city in France").
✅ Ontology-Based: KGs rely on standardized schemas to provide structured, interconnected data.
✅ Designed for Querying, Not Execution: KGs store and retrieve knowledge but don’t execute business processesin the same way as DOGs.
✅ Semantic Reasoning: KGs use AI to infer relationships and meaning across large data sets.
✅ Cross-Domain Knowledge Representation: KGs are designed to unify knowledge across multiple business domains, making them ideal for AI reasoning.
How They Complement Each Other
While DOGs and KGs serve different functions, they are not mutually exclusive—they can work together:
🚀 DOGs can execute AI-driven business processes using real-time data, while KGs can enrich those processes with structured knowledge.
🚀 KGs provide enterprise-wide semantic reasoning, whereas DOGs enable real-time execution at the business process level.
🚀 DOGs help businesses simulate and implement operational workflows, while KGs help structure and store knowledge about those workflows.
For example:
- A Knowledge Graph can define the hierarchy of products in a company’s catalog.
- A Data Object Graph can execute an AI-driven pricing model for those products in real-time.
Why This Matters for AI & Business Agility
Traditional business process models are brittle, requiring long development cycles and centralized governance.
DOGs break this model by allowing:
✅ AI-driven, real-time execution of business workflows
✅ Modular and composable business process design
✅ Faster response to business changes
✅ Seamless integration with AI agents for decision-making
By contrast, KGs provide a strong backbone for structuring enterprise knowledge but do not inherently support execution and automation.
This means organizations need both approaches—one for knowledge representation and reasoning, and another for process execution and business agility.
Final Thoughts: The Right Graph for the Right Job
🚀 If you need an AI-driven system that can execute real-world business workflows, you need a Data Object Graph.
📚 If you need a structured knowledge system that organizes and connects information, you need a Knowledge Graph.
The key is knowing when to use each—and how they can complement each other.
With Dataception Ltd’s Data Object Graphs, AI is just a walk in the park. 🐶🚀