What are knowledge graphs
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What Are Knowledge Graphs?

The world is overflowing with data — scattered across systems, languages, and formats. Knowledge graphs connect information in meaningful ways, enabling better decisions, more accurate search results, and stronger AI performance.

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What is a Knowledge graph?

A knowledge graph is a structured map of information. It represents entities (like products, people, or concepts) as nodes, and the connections between them as relationships or edges. This format allows machines to understand not just data, but the context behind it.

For example, Google's knowledge graph helps distinguish between Apple the company and apple the fruit by recognizing context and relationships.

For a broader perspective on how knowledge graph help organize multilingual data and support content discovery across systems, see our report on taming global content with knowledge graphs.

Why Should You Care?

For Buyers and Enterprises:

  • Improved Search and Discovery

Context-aware search means faster, more relevant results.

  • Data Integration Across Silos

Knowledge graph bring together information from across departments and platforms.

  • Smarter AI Models

AI systems perform better when built on structured, contextualized data.

For Language Service Providers (LSPs):

  • New Revenue Opportunities

Offer knowledge graph solutions to clients as a premium service.

  • Strategic Client Support

Help clients understand and benefit from knowledge graph to elevate your role beyond translation.

How Are Knowledge Graphs Different?

Traditional databases store information in rows and columns. Knowledge graphs go further by capturing the relationships between data points, making them especially useful for complex, multilingual, or global information ecosystems.

They're also language-aware — ideal for companies managing content in multiple languages. In fact, ensuring consistency across these graph depends heavily on the foundational work of terminology management.

Our recent research highlights why terminologists are essential for reliable knowledge graphs ,especially when scalability, and semantic integrity are at stake.

Where Knowledge Graphs Deliver Business Value

Highlighted Use Cases

Knowledge graphs are particularly valuable in multilingual environments. They enable organizations to connect content and concepts across languages without relying on translation alone. Here are two high-impact ways enterprises and LSPs are putting them to work:

1- Cross-Language Search and Information Retrieval

Organizations with global operations often struggle with siloed content in different languages. Knowledge graphs allow users to search in one language and retrieve accurate results across others by connecting equivalent concepts. For example, a search for “Eiffel Tower” can surface results titled Tour Eiffel in French or other localized references, improving enterprise search and customer-facing portals alike.

This case is especially relevant for roles managing global content, internal systems, or digital experience: product owners, heads of IT or localization, and data leads. Sponsors typically include CIOs, CPOs, or CMOs — depending on whether the need is internal search efficiency or external user experience.

2- Personalized, Language-Agnostic Recommendations

Global e-commerce, media, and learning platforms use knowledge graphs to deliver better recommendations. By decoupling content from language and connecting entities like genres, user preferences, or metadata, they can offer personalized results regardless of the original content language. For example, a user watching French documentaries may be shown similar content from other countries — with titles, summaries, and calls to action localized to their preferred language.

You can explore these and other scenarios in the CSA Research report on Fundamentals of Multilingual Knowledge Graphs.

Product executives, heads of personalization, and marketing leaders are often involved in these initiatives. Sponsors typically include Chief Product Officers or Chief Revenue Officers, especially where recommendation engines directly impact user engagement or sales.

Why Is This Important Now?

Data continues to grow — so does the need to make sense of it. Especially for global organizations, integrating multilingual content and extracting meaningful connections is essential. Knowledge graphs offer a scalable way to meet that challenge.


Ready to Put Knowledge Graphs to Work?

Implementing a knowledge graph takes expertise and a clear plan. If you're unsure where to start — or don't have the internal capacity — CSA Research can help. We provide expert guidance to assess your needs, design a practical roadmap, and support implementation.

Explore how leading enterprises use multilingual knowledge graphs to drive global performance — buy the full report series here

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