The most valuable knowledge graphs are flexible and dynamic, adapting to new data types, consumer requirements, and business patterns. However, most knowledge graphs are simply seen as analytical layers above the true source data, and serve a limited scope of analytical cases. In this piece, we will explore how organizations can use knowledge graphs as their system for operational applications in addition to standard analytics. Knowledge Graphs must extend their value from analytical tools to being able to power operational applications across a broad suite of business domains.

Introduction

In the era of information overload, the need for effective data management and utilization has become increasingly important. Knowledge graphs have emerged as a powerful tool for organizing and connecting vast amounts of data, enabling valuable insights and supporting decision-making processes. While knowledge graphs have predominantly been used for analytical purposes, there is immense potential in making them operational, thereby transforming insights into action. This blog post explores the concept of operational knowledge graphs, their benefits, and strategies for making them a practical reality.

Understanding Knowledge Graphs

A knowledge graph is a structured representation of knowledge that captures relationships between entities, attributes, and concepts. It consists of interconnected nodes (entities) and edges (relationships) that provide context and meaning to the data. Traditionally, knowledge graphs have been utilized for analytical tasks, such as data exploration, semantic search, and recommendation systems. However, their potential extends far beyond analysis, enabling organizations to operationalize their knowledge for enhanced decision-making and automation.

Benefits of Operational Knowledge Graphs

Knowledge Graphs shouldn’t just act as a disconnected layer away from your operational data – they should dynamically reflect, inform and drive your business. What if you could make this a reality?

Contextualized Decision-making: By connecting diverse data sources and organizing them into a knowledge graph, organizations can gain a holistic view of their data landscape. This allows decision-makers to access real-time, updated, and contextualized information, leading to more informed and confident decisions.

Efficient Data Integration: Operational knowledge graphs provide a framework for integrating data from multiple sources, such as databases, APIs, and external repositories. This seamless integration enables a unified data model that can be easily accessed and utilized across various applications and systems.

Real-time Insights: With operational knowledge graphs, organizations can leverage real-time data to derive actionable insights. By continuously updating the graph with the latest information, businesses can stay ahead of the curve and respond promptly to changing market conditions.

Automation and Intelligent Systems: Operational knowledge graphs form the foundation for building intelligent systems and automating complex processes. By encoding domain-specific knowledge and rules, organizations can create intelligent workflows, chatbots, and recommendation engines that can make autonomous decisions based on the knowledge graph’s insights.

Making it a reality

At Fluree, we always recommend a four-step cyclical process: model, map, connect and expand. 

Model: Start with a domain and model it — this could be a business application schema, business or industry ontology, or a standardized schema from schema.org. Develop the conceptual model to represent entities, attributes, and relationships within the knowledge graph. Use standard semantic web technologies, such as RDF (Resource Description Framework) to define the schema and capture domain-specific knowledge.

Map: Identify relevant data sources and design a strategy to map them to your model. This may involve data extraction, transformation, and loading processes to ensure data quality and consistency, as well as entity resolution that ensures data is correctly linked and represented within the knowledge graph. 

Connect: Integrate the operational knowledge graph into various applications, systems, and decision-making processes. Once modeled, mapped, and transformed into an operational knowledge Graph database, you can now connect any kind of consumption pattern. This can involve building data-driven applications, embedding the graph into existing systems, or creating APIs that expose the graph’s insights to other applications. A few typical consumption patterns may include:

• Two-Tier Applications
• Data Science Workflows
• LLMs
• Master Data Catalogs

This may also include implementing efficient query and search mechanisms that allow users to explore and retrieve information from the knowledge graph. 

With Fluree, you can provide policy-driven behavior to extend governed read or write access to every user, system, or domain.

Expand: Extend your data model to represent new business domains, continuously update your knowledge graph with new data from existing or new sources, and expand knowledge graph capabilities to empower new users and satisfy new business needs.

Get Started with Fluree

Wherever you are in your Knowledge Graph journey, Fluree can help you accelerate and expand your knowledge graph across domains, power any number of applications, and extend both “read” and “write” capabilities to permissioned users. Learn more here.

Conclusion

Knowledge Graphs are as complex as they are game-changing. As your organization evolves to become more taking steps to bring knowledge graphs closer to operational data will provide your organization with a foundation for success.  It’s important to start small with one domain, prove value, and iteratively build upon early success.