Language agnostic document processing: Finding relations using statistics, machine learning, and graphs

Language agnostic document processing: Finding relations using statistics, machine learning, and graphs

Would you like to be able to find related work regardless of domain or language, more efficiently than you ever thought possible? Omnity is out to help achieve this, using a mix of techniques.

Relations are hard. Any relationship counselor can tell you that, as well as anyone who has ever done research on any topic. Finding what other people have done in your domain is hard work, but it is necessary to properly position and relate your work, discover competitors or collaborators, and improve experimental design.

That has always been the case, but as the pace of innovation and research accelerate, keeping up is getting harder and harder. As someone who has some 120 patents to his name, Brian Sager has been there and done that, and decided he’s had enough.

As it often happens in research, deciding to tackle one issue may have far-reaching implications. Sager, a seasoned R&D professional and serial entrepreneur, saw the potential and decided to found Omnity to commercialize his solution to that problem.

“Documents are much less connected than they could be. Only a small fraction, typically in the area of about 1 percent of all possible references actually exists in the related work section. Why? Because no author knows everything.

Read the full article on ZDNet

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