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.

翻译：我们引入了下一代知识建设和为工业规模知识应用提供动力的平台Saga,Saga采用混合分批设计,不断整合关于现实世界实体的数十亿个事实,并构建一个支持多种生产使用案例的中央知识图,该图涉及数据更新、准确性和可得性等多种要求。在本文中,我们讨论了与工业规模知识图建设相关的独特挑战,并审查了Saga的主要组成部分及其如何应对这些挑战。最后,我们分享了从由Saga驱动的多种生产使用案例中吸取的经验教训。