Growing and Serving Large Open-domain Knowledge Graphs
This work addresses the problem of scaling and applying knowledge graphs for real-world applications like search and ranking, though it appears incremental as it builds on an existing platform.
The paper tackles the challenges of building and using large open-domain knowledge graphs by extending the Saga platform to include a pipeline for training graph embeddings and a semantic annotation service that links web documents to entities, enabling targeted knowledge extraction to enrich the graph and support private on-device knowledge construction.
Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to Saga our platform for continuous construction and serving of knowledge at scale. In particular, we describe a pipeline for training knowledge graph embeddings that powers key capabilities such as fact ranking, fact verification, a related entities service, and support for entity linking. We then describe how our platform, including graph embeddings, can be leveraged to create a Semantic Annotation service that links unstructured Web documents to entities in our KG. Semantic annotation of the Web effectively expands our knowledge graph with edges to open-domain Web content which can be used in various search and ranking problems. Finally, we leverage annotated Web documents to drive Open-domain Knowledge Extraction. This targeted extraction framework identifies important coverage issues in the KG, then finds relevant data sources for target entities on the Web and extracts missing information to enrich the KG. Finally, we describe adaptations to our knowledge platform needed to construct and serve private personal knowledge on-device. This includes private incremental KG construction, cross-device knowledge sync, and global knowledge enrichment.