Knowledge vault: a web-scale approach to probabilistic knowledge fusion
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.
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Even state-of-the-art knowledge graphs suffer from incompleteness.
“Despite such efforts, it is well known that even state-of-the-art KGs suffer from incompleteness .”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [12]
In FreeBase, over 70% of person entities have no known place of birth, and over 99% have no known ethnicity.
“For instance, it has been observed that over 70% of person entities have no known place of birth, and over 99% have no known ethnicity [12, 69] in FreeBase, one of the largest and most widely used KGs for research purposes.”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [12]
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“For instance, it has been observed that over 70% of person entities have no known place of birth, and over 99% have no known ethnicity [12, 69] in FreeBase, one of the largest and most widely used KGs for research purposes.”
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- Authors
- Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., & Zhang, W.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2014-08-24
- Pages
- 601-610
- OpenAlex
- W2016753842
- Cited by
- 1,533
- References
- 1
- Citations by year
- 2026: 41 · 2025: 46 · 2024: 80 · 2023: 112 · 2022: 116 · 2021: 188 · 2020: 184 · 2019: 182 · 2018: 181 · 2017: 144 · 2016: 142 · 2015: 98 · 2014: 19
- FWCI
- 79.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-16-CE23-0007)
- Japan Society for the Promotion of Science
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Keywords
- knowledge base construction, probabilistic knowledge bases, fact extraction, tabular data extraction, manual annotation
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