Through the rise of language models, we’re moving into a world with a growing value of unstructured data. Companies will want to develop capabilities to harness and manage this complex information internally, but to do this they’ll need to move beyond the traditional, relational data model.

Knowledge Graphs and graph-based modeling will be a powerful tool that companies can leverage to model their organizational knowledge and data teams are positioned to play a significant role.

In this post, I’ll explain what knowledge graphs are and how they’re different from the relational data models we’re used to. I’ll also go over how data teams can use them and how they can address existing gaps and be able to deliver these new capabilities.

Understanding Knowledge Graphs

Knowledge graphs structure and represent knowledge through a network of entities (nodes) and their interrelationships (edges). Unlike traditional relational databases, knowledge graphs include semantics, providing context and meaning to the data. This capability makes them particularly suited for handling complex, interconnected information, which is becoming increasingly common in today’s data-driven world.

Knowledge Graphs vs. Relational Data Models

Knowledge graphs differ significantly from relational data models. While knowledge graphs offer a flexible schema that allows for dynamic incorporation of new entities and relationships, relational data models rely on fixed schemas requiring predefined structures. This flexibility in knowledge graphs enables them to adapt quickly to changing data requirements without major reconfigurations.

Furthermore, knowledge graphs excel in handling rich, semantic relationships, making it possible to navigate complex, multi-hop relationships efficiently. In contrast, relational data models manage simple relationships through foreign keys and joins, often leading to performance issues with highly interconnected data.

Knowledge graphs also facilitate seamless integration of heterogeneous data from various sources, including structured, semi-structured, and unstructured formats, whereas relational data models are primarily suited for structured data, making the integration of unstructured information more challenging.

Lastly, knowledge graphs support advanced, relationship-heavy queries with graph-specific languages like SPARQL and Cypher, offering deeper insights and more efficient querying. Relational data models, on the other hand, are optimized for simpler queries and aggregations using SQL, which is less suited for traversing complex relationships.

Implications for Data Teams

As language models and unstructured data become more prevalent, data teams must develop a robust understanding of knowledge graphs to effectively harness this influx of complex, interconnected information. Here are the three most important areas where data teams should focus their efforts:

1. Integration of Structured and Unstructured Data: Develop skills to preprocess and integrate diverse data types into a knowledge graph, leveraging NLP techniques for entity and relationship extraction.
2. Advanced Analytics and Querying: Master the formulation of complex queries and the use of graph algorithms for advanced analytics and insights.
3. Interdisciplinary Collaboration: Collaborate with domain experts and AI teams to build domain-specific ontologies and integrate knowledge graphs with AI models.

Embracing the Future with Knowledge Graphs

As language models and unstructured data continue to grow in importance, knowledge graphs will play a critical role in organizing and leveraging this information. Data teams that develop expertise in knowledge graphs will be well-positioned to unlock new insights, drive innovation, and enhance decision-making processes within their organizations.

In a multi-modal world where data leaders must support diverse AI and unstructured data use-cases, embracing knowledge graphs is not just an option—it’s a necessity. By understanding and integrating knowledge graphs into your data strategy, you can lead your organization into a new era of data management, where complex information is efficiently harnessed, and the full potential of AI is realized.

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