Graph technology has become ubiquitous because it connects relationships based on context. Graphs underpin everything from consumer-facing systems like navigation and social networks, to critical infrastructure like supply chains and policing. Through a combination of data, graphs and semantics (meaning), organisations get a knowledge graph with a deep and dynamic context. In fact, contextual knowledge has emerged as the single most powerful tool that businesses across all industries have for complex decision-making.
Knowledge graphs are useful because they provide a contextualised understanding of data. They achieve this by adding a layer of metadata that imposes rules for structure and interpretation. The report illustrates how using knowledge graphs can help extract greater value from existing data, drive automation and process optimisation, improve predictions, and enable an agile response to changing business environments.
There has been a recent explosion of interest in knowledge graphs, with a myriad of research papers, solutions, analyst reports, groups, and conferences. Knowledge graphs have become so popular partly because graph technology has accelerated in recent years but also because there is strong demand to make sense of data.
External factors have undoubtedly accelerated knowledge graphs to greater prominence. Stresses from the COVID-19 pandemic have strained some organisations to the point of breaking. Decision-making has needed to be rapid, but businesses have been hampered by the lack of timely and accurate insight. Businesses are reconfiguring their operations and processes to be ready to flex rapidly.
As historical knowledge ages faster and is invalidated by market dynamics, many organisations need new ways of capturing, analysing and learning from data. Organisations need to fuel rapid insights and recommendations across the business, from customer experience and patient outcomes to product innovation, fraud detection, and automation.
Moreover, the last few years have demonstrated impressive improvements in Artificial Intelligence (AI) predictive capabilities but with narrow application and some‐ times disturbing results. For AI to reach its full potential, it must incorporate wider contextual information from knowledge graphs. context as the network surrounding a data point of interest that is relevant to a specific AI system. Using knowledge graphs with AI systems is the most effective way to achieve contextual AI, which incorporates neighbouring information, is adaptive to different situations, and is explainable to its users.
Predictions made by AI must be interpretable by experts and ultimately explainable to the public if AI systems are to expand their utility into more sectors. In the absence of understanding how decisions were reached, citizens may reject recommendations or outcomes that are counterintuitive. Explainability is not a nice-to-have—it is a required component of AI, and being context-driven improves explainability.
A knowledge graph is the best way to maintain the context for explainability. It offers a human-friendly way to evaluate connected data, enabling human overseers to better map and visualise AI decision paths. By better understanding the lineage of data (context of where it came from, cleansing methods used, and so forth), data scientists can better evaluate and explain its influence on predictions made by the AI model.
As reported by OpenGov Asia, as organisations across the public and private sectors become increasingly reliant on AI tools and platforms for decision-making, knowledge graphs take on more significance. They offer a comprehensive way to represent data relationships and derive meaning. Knowledge graphs embed intelligence into the data itself and offer AI the tools to make sense of it all – to be more explainable, accurate and repeatable.
