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.
@article{arxiv.2312.09424,
title = {Open Domain Knowledge Extraction for Knowledge Graphs},
author = {Kun Qian and Anton Belyi and Fei Wu and Samira Khorshidi and Azadeh Nikfarjam and Rahul Khot and Yisi Sang and Katherine Luna and Xianqi Chu and Eric Choi and Yash Govind and Chloe Seivwright and Yiwen Sun and Ahmed Fakhry and Theo Rekatsinas and Ihab Ilyas and Xiaoguang Qi and Yunyao Li},
journal= {arXiv preprint arXiv:2312.09424},
year = {2023}
}
Comments
7 pages, 7 figures, 5 tables, preprint technical report, no code or data is released