Ontologies and Knowledge Graphs: Unraveling Complex Data in Financial Services


By John Suit

In the evolving landscape of financial services, the surge in data complexity demands innovative approaches to data management. Semantic technologies, particularly ontologies and knowledge graphs, have emerged as key players in navigating this intricate data maze. But what sets them apart, and how do they complement each other in enhancing data understanding and usability? Let’s dive into the distinctions and synergies between these powerful tools.

Understanding Ontologies: The Semantic Blueprint

At its core, an ontology is a semantic data model designed to define the types of entities within a domain and their interconnecting properties. Think of it as the architectural blueprint for a building, outlining its structure without detailing the specific furniture within each room. In the financial services sector, ontologies provide a generalized framework, identifying broad categories such as “Transactions,” “Clients,” and “Financial Instruments,” along with their relationships and attributes.

For example, an ontology might define:

  • Classes such as Transactions, which encompass all potential financial transactions.
  • Relationships indicating how Transactions involve specific Financial Instruments.
  • Attributes detailing characteristics of these Transactions, like dates or amounts.

This abstract approach enables the reuse of ontologies across different datasets, ensuring a consistent understanding of data types and their relationships.

Knowledge Graphs: The Ontology Applied

Where ontologies sketch the broad strokes, knowledge graphs fill in the vibrant details, mapping out real-world instances of the entities and relationships defined by the ontology. If the ontology is our building’s blueprint, the knowledge graph populates it with every piece of furniture, painting, and decor, transforming it into a lived-in space. In the financial services context, knowledge graphs:

  • Materialize specific Transactions, detailing their attributes and the Clients involved.
  • Reveal connections between Transactions and the Financial Instruments traded.
  • Uncover insights into the geographical distribution of transactions and regulatory implications.

Bridging Theory and Practice

The relationship between ontologies and knowledge graphs is symbiotic. Ontologies offer the structure and universality needed for a scalable data model, while knowledge graphs provide the specificity and depth required for actionable insights.
Consider the ontology-defined relationship “Transaction involves Financial Instruments.” In the knowledge graph, this abstract relationship becomes tangible, detailing how “Transaction ID 123” specifically involves “Stock ABC.” This level of detail transforms abstract models into practical, queryable data landscapes.

Operationalizing Semantic Technologies in Financial Services

In financial services, the fusion of ontologies and knowledge graphs revolutionizes data handling. From enhancing risk management strategies to streamlining compliance reporting, these technologies offer a nuanced understanding of data relationships, empowering firms to:

  • Automate data categorization and processing, reducing manual workload.
  • Improve decision-making by providing a comprehensive view of financial transactions and client interactions.
  • Enhance regulatory compliance by clearly mapping transactions to relevant laws and guidelines.


As we navigate the complexities of data in financial services, ontologies and knowledge graphs stand as beacons of clarity, offering structured yet flexible frameworks for understanding and leveraging data. By marrying the general with the specific, they enable financial firms to harness the full potential of their data, driving innovation, efficiency, and compliance in an increasingly digital world.