Open Domain Knowledge Extraction for Knowledge Graphs
Kun Qian Anton Belyi Fei Wu Samira Khorshidi Azadeh Nikfarjam Rahul Khot Yisi Sang Katherine Luna Xianqi Chu Eric Choi Yash Govind Chloe Seivwright Yiwen Sun Ahmed Fakhry Theo Rekatsinas Ihab Ilyas Xiaoguang Qi Yunyao Li
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.