Summary
A knowledge graph is an organized representation of real-world entities and their relationships. Entities in a knowledge graph can represent objects, events, situations, or concepts. A knowledge graph stores data and relationships alongside frameworks known as organizing principles.
Knowledge graphs are the building blocks of a knowledge graph data model. It amasses facts about people, places, and things into an organized network of entities. Google can tap into the collective intelligence of the knowledge graph to return results tailored to the meaning of your query.
Organizing Principles are a framework that organizes nodes and relationships according to fundamental concepts essential to the use cases at hand. Unlike many data designs, knowledge graphs easily incorporate multiple organizing principles. Think of an organizing principle as a conceptual map or metadata layer overlaying the data and relationships in the graph.
Think of the knowledge graph as a growing, evolving system to simplify your design in the early stages and deliver value sooner. If you pick the right technology to implement your knowledge graph, you can expand and evolve the graph as your needs change.
Property graphs offer superior query performance compared to alternatives like RDF databases or relational databases. A native property graph database traverses relationships by following pointers in memory.
Triple stores express all data in the form of subject-predicate-object “triples” This model does not support relationships with properties or multiple same-typed relationships. Knowledge graphs built on a triple store are more challenging to design, time-consuming to implement, and difficult to change.
Knowledge graphs are emerging as the foundation of AI applications that use proprietary domain data. Industry leaders such as Deloitte highlight the critical role of knowledge graphs for building enterprise-grade GenAI.
Knowledge graph represents the network of suppliers, raw materials, products, and logistics that work together to supply a company’s operations and customers. In Investigative Journalism, knowledge graphs capture key entities (companies, people, bank accounts, etc.) and activities under investigation.
Knowledge graphs underpin insightful applications and artificial intelligence solutions. Download a free copy of the O’Reilly book Building Knowledge Graphs: A Practitioner’s Guide.
