Academic literature can be exceedingly valuable if we know where to look. In order to extract value from all this information, researchers need metaknowledge. This includes familiarity with the most important documents in a field, an understanding of how to contextualize them, a map of the relation between fields, and knowledge of jargon needed to retrieve certain research. West and his team plan to use a text-based search within the network of scholarly citations to extract this metaknowledge. The hierarchical structure of the citation network can guide scholars in their search for important work and might also unveil unexpected connections.
Improving Semantic Search of the Scholarly Literature
In This Section:
- Current Projects
- Aesthetics of Explanation
- Big Questions
- Cognitive and Evolutionary Foundations of Science
- Great Scientists
- Hidden Models
- Idea Generativity
- Levels of Description
- Lives of Concepts
- Machine Science
- Optimal Matching
- Peer Review
- Representations of Knowledge
- Schools of Thought
- Tradition and Innovation
- The Zeitgeist of Science
- Disambiguation Working Group
- Social Sciences Distinguished Lecture Series
- D.E.E.P: Discovering the Extent of Estimable Prediction in Science and Technology
- Social MIND project will build AI models to explain, predict and influence the social world
- New Research
- Our Funders
- Cloud Kotta
News
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James Evans on Social Computing and Diversity by Design
March 15, 2021
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New Postdoctoral Scholars
March 13, 2021
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Journal of Social Computing Launch
December 17, 2020
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Knowledge Lab team wins the IRIS Researcher Award
February 26, 2020
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Analysis of Wikipedia finds politically polarized teams produce better work
March 4, 2019
Connect: