NDLI: Knowledge vault: a web-scale approach to probabilistic knowledge fusion

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Knowledge vault: a web-scale approach to probabilistic knowledge fusion

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Knowledge vault: a web-scale approach to probabilistic knowledge fusion

Content Provider	ACM Digital Library
Author	Heitz, Geremy Gabrilovich, Evgeniy Lao, Ni Murphy, Kevin Horn, Wilko Strohmann, Thomas Zhang, Wei Sun, Shaohua Dong, Xin
Abstract	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 analysis 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.
Starting Page	601
Ending Page	610
Page Count	10
File Format	PDF MP4
ISBN	9781450329569
DOI	10.1145/2623330.2623623
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Knowledge bases Machine learning Information extraction Probabilistic models
Content Type	Audio Text
Resource Type	Article

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