2014 KnowledgeVaultAWebScaleApproach

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Recent years have witnessed a proliferation of large-scale knowledge bases, including Wikipedia, Freebase, YAGO, Microsoft's Satori, and Google's Knowledge Graph. To increase the scale even further, we need to explore automatic methods for constructing knowledge bases. Previous approaches have primarily focused on text-based extraction, which can be very noisy. Here we introduce Knowledge Vault, a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via a nalysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories. We employ supervised machine learning methods for fusing these distinct information sources. The Knowledge Vault is substantially bigger than any previously published structured knowledge repository, and features a probabilistic inference system that computes calibrated probabilities of fact correctness. We report the results of multiple studies that explore the relative utility of the different information sources and extraction methods.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2014 KnowledgeVaultAWebScaleApproachEvgeniy Gabrilovich
Xin Dong
Kevin P. Murphy
Ni Lao
Wei Zhang
Geremy Heitz
Wilko Horn
Thomas Strohmann
Shaohua Sun
Knowledge Vault: A Web-scale Approach to Probabilistic Knowledge Fusion10.1145/2623330.26236232014