In this week’s five-minute interview, we discuss how GraphAware uses natural language processing to help companies gain a better understanding of the knowledge that is spread across their organization.
Ever since we’ve been able to sequence proteins, three-dimensional structures have received tremendous experimental attention. Thanks to the development of new methods and technological advancements, determining these structures has become a more accurate and progressive process over time.
The problem, however, lays in the fact that the progress of discovering new protein structures has not kept pace with the rate at which new sequences are being produced. As a result, we see a continuously growing gap between the number of new sequences being produced and the three-dimensional structures being identified.
In this tutorial, our aim is to write a schema and load it into our knowledge graph;
phone_calls. One that describes the reality of our dataset.
First off, let’s look at the dataset we are going to be working with. Simply put, we’re going to have: