Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale
This addresses the challenge of building scalable knowledge graphs for industrial applications, though it appears incremental as it builds on existing hybrid designs.
The paper tackles the problem of constructing and serving a knowledge graph at industrial scale by introducing Saga, a hybrid batch-incremental platform that integrates billions of facts to support diverse production use cases with requirements for data freshness, accuracy, and availability.
We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to continuously integrate billions of facts about real-world entities and construct a central knowledge graph that supports multiple production use cases with diverse requirements around data freshness, accuracy, and availability. In this paper, we discuss the unique challenges associated with knowledge graph construction at industrial scale, and review the main components of Saga and how they address these challenges. Finally, we share lessons-learned from a wide array of production use cases powered by Saga.