Lessons From Big Tech On Data And Knowledge

The rapid evolution of data-driven industries has led to a paradigm shift in how raw data is processed into information, then knowledge, to be exploited for artificial intelligence (AI) applications. Large technology companies have clearly demonstrated the efficacy of embracing knowledge graphs and ontologies, how these tools underpin modern AI and data management, and the transformative potential they hold for industries as diverse as self-driving cars, digital recommendations, and even global mining. Many case studies from leading tech giants – such as Google, Microsoft, Amazon, and Meta – show the importance and strategic value of adopting these technologies.

In today’s data-saturated landscape, managing, understanding, and leveraging vast and varied datasets is crucial for competitive advantage. Knowledge graphs and ontologies have emerged as pivotal technologies, enabling companies to capture and integrate complex relationships between disparate data points. To get a better understanding of these technologies, and the critical role they play in elevating data management to an entirely new level, this article will review the reasons why major tech companies adopted knowledge graphs and ontologies into their systems, how these technologies facilitate next-level data architectures capable of evolving with the demands of AI, and the implication of these new data structures for non-traditional tech sectors, such as mining.

Knowledge graphs and ontologies: An overview

Major technology companies have increasingly embraced knowledge graphs and ontologies as fundamental components of their data architecture. This adoption is driven by these technologies’ unique ability to represent complex relationships and contextual information in a machine-readable format. Knowledge graphs are data structures that interlink entities (such as people, objects, and concepts) in a graph format, capturing not only the entities’ details, but also the relationships between them. They offer a semantic layer that makes data more accessible and interpretable for machines and humans. This technology is particularly beneficial for recommendation systems, as well as search and query efficiency. Amazon, Netflix, and Spotify have revolutionised their recommendation engines by implementing knowledge graphs. These graphs capture intricate relationships between products, content, and user preferences. Amazon uses knowledge graphs to understand product relationships, user behavior patterns, and purchase histories, enabling it to suggest complementary products and anticipate customer needs. Netflix employs knowledge graphs to map connections between shows, genres, actors, and viewer preferences, resulting in highly personalised content recommendations. Spotify’s knowledge graph connects artists, genres, listening patterns, and musical attributes to produce sophisticated playlist recommendations and discover emerging musical trends.

Google, Microsoft, and Meta have integrated knowledge graphs and ontologies into their core operations. Google’s knowledge graph is built to enhance search results with contextual information and related concepts. Microsoft’s knowledge graph powers various services, including Bing and LinkedIn, to provide more intelligent search and networking capabilities. Meta uses knowledge graphs to understand social connections, content relationships, and user interests for improved content delivery and ad targeting.

This is a preview of an article that was originally published in the May 2025 issue of Global Mining Review.

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