Exploiting cognitive computing and frame semantic features for biomedical document clustering

Nowadays, there are plenty of text documents in different domains that have unstructured content which makes them hard to analyze automatically. In particular, in the medical domain, this problem is even more stressed and is earning more and more attention. Medical reports may contain relevant information that can be employed, among many useful applications, to build predictive systems able to classify new medical cases thus supporting physicians to take more correct and reliable actions about diagnosis and cares. It is generally hard and time consuming inferring information for comparing unstructured data and evaluating similarities between various resources. In this work we show how it is possible to cluster medical reports, based on features detected by using two emerging tools, IBM Watson and Framester, from a collection of text documents. Experiments and results have proved the quality of the resulting clusterings and the key role that these services can play.