Knowledge graphs on the rise: Gartner® 2024 Hype Cycle™ for AI shows their growing impact

The 2024 Gartner® Hype Cycle™ for Artificial Intelligence, released earlier this year, offers crucial insights into the evolving landscape of AI technologies. As a leader in life sciences data and knowledge discovery, ONTOFORCE is keenly aware of how these trends impact our industry, particularly with the growing recognition of knowledge graphs.

Gartner's 2024 report places knowledge graphs on the "Slope of Enlightenment," highlighting their increasing maturity and essential role in enterprise AI strategies. This recognition underscores a pivotal shift in the industry as more life sciences organizations realize the transformative potential of knowledge graphs, particularly when integrated with AI and machine learning (ML).

The crucial role of knowledge graphs in life sciences

In the complex world of life sciences, data is not just vast; it's multifaceted and deeply interconnected. Knowledge graphs, with their ability to model relationships between entities—whether genes, diseases, compounds, or clinical trials—offer unparalleled advantages in organizing and analyzing this complexity. By structuring data in a way that reflects real-world interconnections, knowledge graphs enable more nuanced insights, paving the way for breakthroughs in research, drug development, and patient care.

Reflecting on the growing significance of knowledge graphs, Valerie Morel, CEO of ONTOFORCE, shared: "I'm pleased to see the growing attention that knowledge graphs are receiving these days, increasingly with the rise of GenAI. In an era where the life sciences industry is driven by vast amounts of data, knowledge graphs are emerging not only as the linchpin that connects disparate datasets into a coherent and actionable framework but also as an indispensable part of an enterprise ecosystem that increases the success rate of AI and ML initiatives. At ONTOFORCE, we're committed to empowering organizations to unlock the full potential of their data, driving innovation and improving patient outcomes."

As AI and ML technologies advance, their effective application in life sciences hinges on the quality and structure of the underlying data. Knowledge graphs serve as a critical enabler for these technologies by providing a robust framework that ensures data is both comprehensive and contextually relevant. When combined with AI and ML, knowledge graphs facilitate the development of more sophisticated models that can predict outcomes, identify trends, and generate new hypotheses in areas like drug discovery and personalized medicine.

Why 2024 is a pivotal year for knowledge graphs

At the Gartner D&A conference in London in May, Mark Beyer, Research Vice President and Distinguished Analyst at Gartner, highlighted "Add Semantic Data Integration & Knowledge Graphs" as one of the Top 10 trends in Data Integration and Engineering. To our knowledge, this inclusion is a first, and it clearly signals Gartner's view on knowledge graphs: knowledge graphs are foundational to modern data strategies. We would like to add this: especially in industries, such as life sciences, where data complexity demands the integration of AI and ML to generate more meaningful insights.

The shift in Gartner's Hype Cycle signifies a turning point for knowledge graphs in 2024. As organizations grapple with ever-increasing volumes of complex data, the ability to effectively integrate and analyze this data becomes crucial. Knowledge graphs offer a sophisticated solution by providing a structured, interconnected view of diverse data sources, which can be harnessed by AI and ML algorithms for deeper analysis.

This year, the industry recognizes knowledge graph's value for enhancing data management and driving tangible business outcomes through AI-powered innovation. With advancements in AI and increased adoption of knowledge graphs, life sciences companies are now better positioned to leverage their data for accelerated innovation, streamlined operations, and more tailored and effective treatments.

Real-world impact: use cases in the life sciences industry

Consider a pharmaceutical company navigating the complexities of drug development. With a knowledge graph, they can seamlessly integrate and analyze data from target identification, hit identification, lead optimization, in vitro and in vivo testing, preclinical studies, clinical trials, post-market surveillance, and more. In assembling each piece of evidence in an integrated matter on the knowledge graph, hidden patterns and insights are uncovered that might be the key to shortening certain development processes and thus accelerating time-to-market. With the help of AI and ML models, this can go even faster.

A few examples:

• In translational research, knowledge graphs can be especially pivotal for drug repurposing efforts. By connecting diverse datasets—such as chemical structures, biological pathways, and clinical outcomes—and applying AI-driven analysis, researchers can identify new therapeutic uses for existing drugs, significantly speeding up the discovery process.
• On the clinical side, knowledge graphs can improve patient stratification for clinical trials. By integrating genomic data, patient histories, and previous trial outcomes and applying ML algorithms, researchers can pinpoint specific patient subgroups more likely to respond to a new treatment. This targeted, AI-driven approach not only improves the success rates of trials but also ensures that patients receive the most effective therapies, reducing both costs and timelines.
• In the regulatory domain, knowledge graphs can streamline the process of regulatory submissions. By integrating and organizing data from various regulatory guidelines, previous submissions, and clinical data and using AI to ensure compliance, companies can reduce the risk of delays and rejections, speeding up the approval process and bringing new therapies to market more efficiently.

The future of knowledge graphs in life sciences

As we look to the future, the fusion of knowledge graphs with AI and ML technologies, including retrieval augmented generation (RAG), will only amplify their collective impact. By combining structured data from knowledge graphs with unstructured data sources, life sciences companies can achieve unprecedented levels of accuracy and insight, whether in predictive analytics, risk management, or patient care.

At ONTOFORCE, we are at the forefront of this transformation, empowering life sciences organizations to harness the full potential of their data. With DISQOVER, our knowledge discovery platform built on knowledge graph technology, we enable the industry to confidently make data-driven decisions, ensuring that the next big breakthrough is always within reach.

Gartner has recognized ONTOFORCE and its platform DISQOVER in several recent Hype Cycles for Life Sciences:

• "Hype Cycle for Life Science Discovery Research, 2024"
• "Hype Cycle for Life Science Clinical Development, 2023" 
• "Hype Cycle for Life Science Discovery Research, 2023"

These acknowledgments by Gartner highlight ONTOFORCE's role in advancing clinical development and drug discovery through our innovative semantic technology.

Connect with our experts today to learn more about how knowledge graphs can revolutionize your organization's approach to data. Let us show you how DISQOVER can turn your complex data into actionable insights, driving innovation to ultimately improve outcomes for patients.