From the course: Introduction to Probabilistic Knowledge Graphs: AI-Driven Inference and Real-World Applications

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Structured data for PKGs

Structured data for PKGs

From the course: Introduction to Probabilistic Knowledge Graphs: AI-Driven Inference and Real-World Applications

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Structured data for PKGs

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When we talk about structured data, think of organized rows and columns, tables or spreadsheets, where every column has a name, a type, and a defined meaning, like product name is text, price is numeric, category is a controlled label. Primary keys uniquely identify rows, foreign keys define links between tables. Because structure and semantics are explicit, the machine doesn't have to guess. Schema first, guessing later. Why does this matter for a probabilistic knowledge graph? Because structured data already encodes entities and relationships. If your data set includes a field like born in city ID, that's not just a column. It's a relationship waiting to become an edge. Schema tells us what connects to what. Let's see this in practice. Supports, we have two tables. person with the ID name and born in city ID column, and the city table with the ID and name column. Each person row becomes a node labeled person. Each city row becomes a node labeled city. Now notice the field born in…

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