University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Auto-populating ontologies: Data-extraction beyond single properties with ChemDataExtractor 2.0 and TableDataExtractor

Auto-populating ontologies: Data-extraction beyond single properties with ChemDataExtractor 2.0 and TableDataExtractor

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The abundance of data found in heterogeneous sources, such as scientific publications, has forced the development of automated techniques for data extraction. Indeed, in many data-driven methodologies for materials science, the biggest problem is the lack of usable data to begin with, with databases being expensive to build and maintain.

With ChemDataExtractor 2.0 in combination with TableDataExtractor, we present a framework that goes beyond the extraction of single properties and enables the auto-population of user-defined ontologies of interest.

Thus, getting closer to seamless integration of heterogeneous data sources into the data-driven research framework.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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