Keeping Knowledge Graphs Alive: Constructing, Growing, and Sustaining Knowledge Graphs for the Long Haul
Authors: Oshani Seneviratne
Venue: WWW
Year: 2025

Knowledge graphs (KGs) have emerged as fundamental infrastructure for organizing and structuring vast amounts of information across various domains. However, building and maintaining large-scale KGs presents significant challenges-not only in terms of initial construction but also in ensuring their long-term sustainability. Traditional approaches to KG development and maintenance often rely on institutional funding, making them vulnerable to resource constraints. In this keynote, I will discuss alternative community-driven models for sustainable knowledge graph construction and maintenance, emphasizing grassroots-level engagement, decentralized curation, and incentivized reuse of knowledge assets. I will discuss how leveraging collaborative contributions, open governance models, and incentive structures-including blockchain-based provenance tracking and reputation systems-can promote long-term commitment without dependence on centralized management. Through real-world examples, including lessons from the development and maintenance of the FoodKG, I will outline the key technical, social, and economic challenges in constructing a large-scale knowledge graph that remains both useful and adaptable over time. FoodKG was built as a structured representation of over one million recipes, including their ingredients, nutrients, and relationships to various dietary constraints, all expressed in RDF to ensure flexibility and interoperability. By leveraging linked data principles, FoodKG integrates diverse sources, such as nutritional databases, upper-level food ontologies, food composition datasets, and information from recipe websites, creating a rich resource that can support AI-driven applications in the food and health domain. Since its creation, the FoodKG has been applied in various real-world scenarios, demonstrating its value across multiple domains. Some examples include: (1) Personalized diet recommenders that use FoodKG to generate meal plans based on users' dietary needs, restrictions, and preferences; (2) Innovative recipe extraction with ingredient substitutions, making cooking more inclusive for people with allergies or specific health conditions; and (3) Health assistant chatbots that leverage FoodKG to provide context-aware nutritional guidance, helping users make informed food choices. While FoodKG has proven to be a valuable resource, its long-term impact hinges on its ability to evolve beyond its initial design. Community-driven extensions-such as the integration of new recipes, cultural food traditions, and emerging nutritional insights-can significantly expand its scope and relevance. Fortunately, the inherent flexibility of the underlying semantics allows for the seamless incorporation of new data sources. However, curating knowledge through open contributions presents key challenges, particularly in ensuring accuracy, consistency, and reliability. To address this, we need robust validation workflows that combine automated checks, expert reviews, and crowd-sourced verification. Additionally, incentivizing high-quality contributions is essential for sustaining engagement. Reward mechanisms-such as attribution-based credit systems, reputation scores, and micro-rewards tied to the future usage of knowledge assets-can motivate contributors and establish a self-sustaining ecosystem where knowledge is continuously expanded and refined. Ultimately, rethinking sustainability models in KG development is not just a technical challenge, but a paradigm shift. By embracing open, collaborative, and incentivized approaches, we can transform knowledge graphs into living, evolving resources-not just static datasets, but dynamic ecosystems that empower researchers, practitioners, and the broader public alike. The future of knowledge graphs depends not just on how we build them, but on how we sustain and grow them together.