We live in an era when information is growing at an unprecedented rate, but not always in a structured or comprehensible way. In this context, knowledge graphs are gaining prominence as crucial tools for organising, contextualising, and making information useful. These structures are revolutionising how we access information and how we make decisions based on knowledge.
More than just a passing technological trend, knowledge graphs represent a necessary evolution in today’s digital ecosystems. Their application extends from artificial intelligence to decision support systems, with a direct impact on the competitiveness and efficiency of organisations.
But, after all, what are knowledge graphs?
Knowledge graphs are structured representations of knowledge, where concepts (or entities) such as people, places, events or objects are linked together by semantic relationships. Imagine a network in which each node represents a concrete idea or element, and each connection between nodes indicates a meaningful relationship – for example, “Paris is the capital of France” or “Maria works at Company X”. Unlike traditional databases, which store data in isolation, knowledge graphs capture context and meaning, facilitating the “automatic” understanding of information by computer systems.
This approach is especially valuable in complex and dynamic environments, where data comes from different sources and is constantly growing. Tools such as Google Knowledge Graph, Netflix’s recommendation systems, and virtual assistants such as Alexa and Siri are practical examples of how these structures shape our daily digital lives. Using technologies such as ontologies and linked data, knowledge graphs allow us to infer new information from existing relationships, promoting a more prosperous and integrated view of knowledge.
And why are they relevant today?
In a scenario marked by the explosion of data and the growing dependence on intelligent systems, knowledge graphs offer a clear response to the challenge of information disorganisation and opacity. Their relevance lies in their ability to generate contextualised and verifiable knowledge from large volumes of heterogeneous data. Instead of simply collecting or storing data, these structures allow for data interpretation and interconnection and are used strategically and semantically coherently. They also make a decisive contribution to developing explainable artificial intelligence, ensuring that AI systems make decisions humans can understand and trust. Knowledge graphs make machines’ reasoning processes more transparent by providing explicit relationships between concepts. This feature is particularly valued in sectors such as health, finance or justice, where trust and traceability of information are essential.
In which sectors are knowledge graphs making a difference?
Knowledge graphs are already profoundly transforming several sectors of activity. In the health sector, for example, they integrate clinical data, scientific literature and biomedical knowledge, enabling more accurate diagnoses and personalised treatments. By interconnecting symptoms, medications, studies and genetic profiles, knowledge networks help doctors and researchers make evidence-based decisions.
These structures are essential in fraud detection and risk analysis in the financial sector. By mapping relationships between entities, transactions, and behavioural patterns, suspicious connections or inconsistencies that would escape traditional systems can be identified. Knowledge graphs are also gaining ground in the cultural and heritage sector. They enable digital contextualisation and preservation of historical collections, linking works, authors, periods, and styles dynamically and navigably.
In public administration, these technologies have been applied to promote interoperability between systems and improve the provision of services to citizens. Relationally organising government information facilitates access, increases efficiency, and reduces redundancies between departments.
How to start exploring this resource?
The first step for organisations that want to adopt knowledge graphs is to identify a clear and well-defined knowledge domain – customers, products, legislation or another critical area. Next, it is essential to map the available data, ensure its quality and design an ontology that defines the key concepts and their relationships. Several accessible tools for building knowledge graphs, from open-source solutions such as Neo4j, RDF4J and GraphDB, to commercial platforms with more user-friendly visual interfaces and integration with inference and analysis engines.
In addition to the technical aspect, it is essential to involve business experts in the process, ensuring that the graph reflects the organisation's reality and needs. With a solid foundation and well-defined objectives, it is possible to begin extracting real value from these structures, whether through dashboards, internal search engines, recommendation systems, or knowledge-based AI models.
What can we conclude?
Knowledge graphs are more than a technical innovation: they are a paradigm shift in organising and interpreting information. In a world where data and information are abundant but often disconnected, these structures provide the map that allows us to navigate confidently. As organisations seek to make smarter, faster, and more accountable decisions, knowledge graphs are a strategic investment and a competitive differentiator.
Opinion article by: Tiago Pereira, Senior Researcher in Software Engineering
