Open Domain Knowledge Extraction for Knowledge Graphs
This addresses the problem of building and maintaining industry-scale open domain knowledge graphs for applications like question answering, though it appears incremental as it focuses on framework design and deployment lessons.
The paper tackles the challenge of ensuring completeness and freshness in knowledge graphs by introducing ODKE, a scalable framework that extracts high-quality entities and facts from the open web at scale, supporting both streaming and batch processing.
The quality of a knowledge graph directly impacts the quality of downstream applications (e.g. the number of answerable questions using the graph). One ongoing challenge when building a knowledge graph is to ensure completeness and freshness of the graph's entities and facts. In this paper, we introduce ODKE, a scalable and extensible framework that sources high-quality entities and facts from open web at scale. ODKE utilizes a wide range of extraction models and supports both streaming and batch processing at different latency. We reflect on the challenges and design decisions made and share lessons learned when building and deploying ODKE to grow an industry-scale open domain knowledge graph.