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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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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Abstract 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.
AbstractList 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.
Author Sang, Yisi
Qian, Kun
Chu, Xianqi
Khorshidi, Samira
Sun, Yiwen
Govind, Yash
Luna, Katherine
Belyi, Anton
Rekatsinas, Theo
Khot, Rahul
Choi, Eric
Wu, Fei
Ilyas, Ihab
Li, Yunyao
Qi, Xiaoguang
Fakhry, Ahmed
Nikfarjam, Azadeh
Seivwright, Chloe
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BackLink https://doi.org/10.48550/arXiv.2312.09424$$DView paper in arXiv
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Computer Science - Computation and Language
Title Open Domain Knowledge Extraction for Knowledge Graphs
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