Maximizing Innovation Potential with Knowledge Graphs

The Business Impact of the Foodpairing KG: A Deep Dive into Efficiency, Collaboration, and Cost Reduction Strategies
Written by Stratos Kontopoulos - 03.04.2024

A Knowledge Graph (KG in short) is a structured data model that represents knowledge as a network of interconnected entities and their interrelationships. Being highly flexible, KGs can incorporate diverse data sources, making them powerful tools for providing a unified view for better decision-making, streamlined data discovery, and enhanced analytics. Therefore, for enterprises in virtually any domain, KGs constitute powerful AI tools that drive innovation, efficiency, and competitiveness by empowering them to leverage their data assets effectively. To further support this claim, Gartner’s Impact Radar for 2024 places KGs at the core of emerging technologies, under the critical enablers quadrant. 


The Foodpairing Knowledge Graph

To use AI in product innovation effectively, you need the right kind of data along with the right connections between different datasets. At Foodpairing AI we are deploying and continuously extending our enterprise KG that organizes a vast array of food and flavor data into a coherent network of interconnected entities and relationships, making us a pioneer regarding the use of these technologies in the domain. Integrating inputs from our in-house food scientists and culinary experts, as well as from proprietary algorithms and publicly available third-party sources, the Foodpairing KG drastically facilitates advanced data analytics and unlocks invaluable insights into ingredient interactions, emerging food trends, flavor profiles, and recipe recommendations.

Assessing the Business Value

Counting almost two years since its birth, and having been integrated enterprise-wide in almost all our operations, we decided to conduct an internal exercise that would assess whether the deployment of the Foodpairing KG indeed delivers substantial business value for the enterprise. The following paragraphs discuss the relevant key time- and cost-related business value accelerators.

Time Savings & Efficiency

The integration of the KG as the main tool for calculating the novelty of candidate ingredient combinations has resulted in significant time savings and efficiency gains. Previously, the process required approximately 16 hours to complete, but with the introduction of the KG, the time has been reduced to a mere 2 hours, representing an impressive 80% reduction in time. This remarkable improvement not only enhances our team’s productivity but also allows for quicker exploration of novel flavor pairings and innovative recipe development.

Data Interoperability & Re-use

The enhanced data interoperability and re-use capabilities within the Foodpairing KG have facilitated the amalgamation of market insights through advanced queries, resulting in substantial time gains ranging from 30% to 70%. Previously, retrieving and combining market data required extensive manual effort and time-consuming processes for our Solution Delivery Team. However, with the advanced query capabilities of the KG, this task has been streamlined, allowing for quicker access to comprehensive market insights. This efficiency not only saves valuable time but also opens the door to applying actionable intelligence to inform strategic business decisions (incl. portfolio and product development strategy) and drive growth.

Streamlined Cross-Functional Collaboration

The Foodpairing KG serves as a centralized platform for streamlined cross-functional collaboration within the company, significantly reducing the time spent on coordination by approximately 70%. Previously, coordinating efforts across different teams involved numerous communication channels and manual processes, leading to unavoidable inefficiencies and delays. However, with the KG providing a unified space for data sharing and collaboration, our teams can now seamlessly exchange information, insights, and ideas, fostering a more agile and responsive working environment. This reduction in coordination time not only improves productivity but also enhances communication and alignment across the organization.

Business Contributions

Using the KG as the basis for deploying graph embeddings has yielded invaluable insights into sales predictions. The KG can effectively model and analyze complex relationships between various entities, such as ingredients, recipes, and market trends. The use of graph embeddings on top empowers our teams with more accurate sales predictions, enabling businesses to anticipate market demands, optimize inventory management, and capitalize on emerging trends. This not only enhances strategic decision-making but also drives profitability and competitiveness, highlighting the transformative impact of advanced data analytics within food product development and sales forecasting.

Reduced R&D Costs

By spotlighting underutilized ingredients for specific flavor profiles, the KG facilitates a more targeted approach to R&D teams in food and beverage companies, leading to a notable reduction in costs. We estimated a substantial reduction of approx. 25% in research costs in our attempts to identify and capitalize on overlooked opportunities, optimizing resource allocation and minimizing unnecessary expenditures, which allows us to approach a delicate balance between cost-efficiency and creative exploration. In our pursuit of leveraging the insights provided by the KG, at Foodpairing AI we aim to streamline our R&D processes, unlocking new avenues for product development and staying ahead in a competitive market landscape.




To use AI in product innovation effectively you need to right datasets as well as the right connections between different data sets correctly mapped out. The Foodpairing Knowledge Graph (KG) stands as a pivotal tool in revolutionizing flavor innovation within the food industry. Integrating inputs from in-house experts, proprietary algorithms, and third-party sources, the KG streamlines advanced data analytics, uncovering invaluable insights into ingredient interactions, emerging food trends, flavor profiles, and recipe recommendations. Our internal assessment revealed significant time and cost savings, streamlined cross-functional collaboration, and enhanced strategic decision-making due to the use of the KG enterprise-wide. With the KG’s ability to optimize resource allocation, minimize expenditures, and spotlight underutilized ingredients, Foodpairing AI is poised to maintain its position at the forefront of flavor exploration and product development.

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