Open Domain Knowledge Extraction for Knowledge Graphs

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...

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Bibliographic Details
Main Authors Qian, Kun, Belyi, Anton, Wu, Fei, Khorshidi, Samira, Nikfarjam, Azadeh, Khot, Rahul, Sang, Yisi, Luna, Katherine, Chu, Xianqi, Choi, Eric, Govind, Yash, Seivwright, Chloe, Sun, Yiwen, Fakhry, Ahmed, Rekatsinas, Theo, Ilyas, Ihab, Qi, Xiaoguang, Li, Yunyao
Format Journal Article
LanguageEnglish
Published 30.10.2023
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computation and Language
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Summary: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.
DOI:10.48550/arxiv.2312.09424