When it comes to knowledge graphs, I've learned that simplicity is your ally. The 80/20 rule serves me well—focus on modeling the 20% of relationships that drive 80% of your value. At Fulfill.com, we initially tried to map every possible variable in the 3PL matching process—SKU dimensions, carrier relationships, integration capabilities—you name it. We quickly found ourselves drowning in a sea of connected nodes that became unwieldy to maintain and actually slowed our decision-making. Now we take a more pragmatic approach. We start by identifying the core entities that matter most—in our case, merchants, 3PLs, fulfillment centers, and shipping zones. Then we map the critical relationships between them, like geographic coverage or specialized capabilities for certain product types. I recommend implementing these practical guardrails: First, be ruthless about your ontology. Every entity or relationship type you add creates exponential complexity. Ask: "Does this distinction meaningfully improve outcomes?" If not, simplify. Second, use abstraction layers to manage complexity. We've built our knowledge graph to handle high-level concepts like "temperature control capability" rather than getting lost in the weeds of specific refrigeration equipment models. Third, ensure your data collection processes are sustainable. The best knowledge graph in the world becomes useless when populated with stale data. Design your system so updates happen naturally through business processes. Finally, remember that knowledge graphs exist to solve problems, not to represent perfect reality. When our customers need a 3PL that can handle hazardous materials in the Pacific Northwest, they don't need to understand every detail of HAZMAT certification—they just need the right match. The true test is whether your knowledge graph makes decisions better and faster. Keep it practical, focus on the relationships that drive outcomes, and you'll build something that actually scales with your business.
The golden rule: model just enough to answer the questions you actually need to ask—nothing more. Trying to capture every nuance turns your graph into a tangled mess that's impossible to maintain. Start with high-value entities and relationships that drive real use cases, and only go deeper when there's a clear payoff. Think of your knowledge graph like a map—you don't need every tree and pebble, just the roads that get you where you're going.