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Generating accurate ranking algorithms via machine learning

Christine Dumoulin
Christine Dumoulin
Project Description: 

Metadata — data that describe other data — accompany every scientific data set, and their richness, quality, and interoperability determine how well data sets can be found, understood, and re-used. A number of standards are used to organize metadata, and their structural differences create difficulties for data sharing and other collaborative efforts. Metadata crosswalks remove the barrier to interoperability by providing a map between one standard and another. Crosswalks are difficult to generate because they often consist of too many terms to map by hand, and are written in natural language that cannot be effectively processed by software alone. I have written a program with which a user can construct and evaluate processing algorithms without the need to write new code. This tool allows users to quickly compare different algorithms which are suited to build crosswalks between the metadata standards that are relevant to their work

Primary Mentor: 
Bruce Wilson
Secondary Mentor: 
Dave Vieglais, Giri Palanisamy