Opportunities expanded by the new breed of scholarly databases

Abstract

While a wide range of scholarly information becomes available online and standardized, the sheer volume and diversifying content types make it challenging to structure the data for cross-analysis. In recent years, new scholarly databases are being released attempting to overcome such challenges by leveraging natural language processing and machine learning algorithms, or automating the metadata acquisition process. This article offers an overview of the factors enabling the new breed of databases with selected examples to discusses how such new tools would influence the future of the scholarly information market.