Many aspects of macroevolutionary theory and our understanding of biotic responses to global environmental change derive from literature-based compilations of paleontological data. Existing manually assembled databases are, however, incomplete and difficult to assess and enhance with new data types. Here, we develop and validate the quality of a machine reading system, PaleoDeepDive, that automatically locates and extracts data from heterogeneous text, tables, and figures in publications. PaleoDeepDive performs comparably to humans in several complex data extraction and inference tasks and generates congruent synthetic results that describe the geological history of taxonomic diversity and genus-level rates of origination and extinction. Unlike traditional databases, PaleoDeepDive produces a probabilistic database that systematically improves as information is added. We show that the system can readily accommodate sophisticated data types, such as morphological data in biological illustrations and associated textual descriptions. Our machine reading approach to scientific data integration and synthesis brings within reach many questions that are currently underdetermined and does so in ways that may stimulate entirely new modes of inquiry.
Department of Geoscience, University of Wisconsin-Madison, Madison, Wisconsin, United States of America;Computer Sciences Department, University of Wisconsin-Madison, Madison, Wisconsin, United States of America;Computer Sciences Department, University of Wisconsin-Madison, Madison, Wisconsin, United States of America;Department of Computer Science, Stanford University, Stanford, California, United States of America
Recommended Citation:
Shanan E. Peters,Ce Zhang,Miron Livny,et al. A Machine Reading System for Assembling Synthetic Paleontological Databases[J]. PLOS ONE,2014-01-01,9(12)