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Web-scale information extraction in knowitall: (preliminary results)
Abstract
Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.
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Cited by
Yago: a core of semantic knowledge
Suchanek, F. M., Kasneci, G., & Weikum, G. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242667 · Cited by 2,512
Commonsense reasoning and commonsense knowledge in artificial intelligence
Davis, E., & Marcus, G. (2015) · Communications of the ACM · doi:10.1145/2701413 · Cited by 434
Progress in Neural NLP: Modeling, Learning, and Reasoning
Zhou, M., Duan, N., Liu, S., & Shum, H.-Y. (2020) · Engineering · doi:10.1016/j.eng.2019.12.014 · Cited by 281
Named Entity Recognition and Relation Extraction: State-of-the-Art
Nasar, Z., Jaffry, S. W., & Malik, M. K. (2022) · ACM Computing Surveys · doi:10.1145/3445965 · Cited by 245
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
Metadata
- Authors
- Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D. S., & Yates, A.
- Venue
- Proceedings of the 13th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2004-05-17
- Pages
- 100-110
- OpenAlex
- W2115461474
- Cited by
- 747
- References
- 0
- FWCI
- 33.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D. S., & Yates, A. (2004). Web-scale information extraction in knowitall: (preliminary results). Proceedings of the 13th international conference on World Wide Web. https://doi.org/10.1145/988672.988687
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
