When Google introduced the term, they didn’t define it formally. Instead, they showcased its ability to transform search from “strings to things,” moving beyond keywords to understanding and delivering contextual answers.
The idea is simple: Imagine searching for "Sagrada Familia" and not just getting a list of web pages, but also a rich summary box (a "knowledge panel") packed with relevant facts: it’s a stunning church in Barcelona, designed by Antoni Gaudi. This shift doesn't just enhance search results; it opens up a world of possibilities, like booking flights or shopping online directly from your search results.
So, what exactly is a knowledge graph? A knowledge graph captures information about foundational (key/main) entities in a domain or a business and the relationships between them. For developers, it’s a powerful database with an API. For data scientists, it’s a goldmine of enriched data for machine learning. For data engineers, it’s a robust data integration tool. And for business users, it’s an intuitive way to interact with data.
Knowledge graphs make data smarter by centralizing and standardizing definitions, ensuring consistency across applications. They use a semantic layer (ontology) to define types of entities and relationships, enabling rich, automated inferences. This means smarter, more contextual insights at your fingertips.
Consider this: combining facts in the graph with the reasoning power of the ontology, you get richer, more insightful answers. It’s like having a supercharged brain for your data, capable of sophisticated analysis and exploration.
Understanding and leveraging knowledge graphs can transform how you interact with and interpret data. Stay tuned as we dive deeper into their implementation, processing capabilities, and exciting intersections with AI and large language models.
The new O’Reilly book Building Knowledge Graphs: A Practitioner’s Guide is available for free on our site for a limited period. Grab your copy today and master the art of building knowledge graphs.
