A new machine-learning tool from an artificial intelligence (AI) startup used 30,000 Wikipedia entries to create a model that allowed it to identify the characteristics that make a scientist noteworthy enough for encyclopedic inclusion. It mined the academic search-engine Semantic Scholar to identify 200,000 scholars in a variety of fields. To complete the process, it is systematically composing Wikipedia entries for scholars on its list who are missing from the encyclopedia. Boing Boing brought this interesting information to our attention in their article, “A machine learning system trained on scholarly journals could correct Wikipedia’s gendered under-representation problem.”

Quicksilver doesn’t directly edit Wikipedia. It drafts entries and revisions for humans to refer to in improving the encyclopedia. In addition to creating new Wikipedia entries, Quicksilver can suggest new material for existing entries.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction.

Melody K. Smith

Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.