University of Pittsburgh

To develop software and services for transforming mathematical results as they appear in journal article abstracts into formally structured data that machines can read, process, search, check, compute with, and learn from as logical statements

  • Amount $582,852
  • City Pittsburgh, PA
  • Investigator Thomas Hales
  • Year 2018
  • Program Technology
  • Sub-program Scholarly Communication

Computers do nothing but process logical statements. Mathematics consists of nothing but such statements. It would be reasonable to assume, then, that computers would be adept, perhaps uniquely, at reading, understanding, and cataloging the academic literature of mathematics. Not yet. People and machines, it turns out, speak different mathematical languages. If computers are to help manage mathematical knowledge, they need to be taught how to read math papers. The grant funds efforts by mathematician Thomas Hales to begin that instruction. Hales has raised an international army of graduate students and postdoctoral researchers, which he plans to unleash on the abstracts of thousands of mathematical papers. They will carefully translate the definitions and results that appear in these abstracts into formal programming language. These formalized abstracts—“fabstracts,” for short—can then be used to train machine learning algorithms to “read” textual mathematics.   

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