Digital transformation in life sciences has fundamentally changed the ways researchers access and manage scientific knowledge. Analytics allow us to deal with the complexity of data, but researchers cannot easily access valuable latent connections from knowledge embedded in the published literature.
We have undertaken the mission of building a robust data architecture for machine reasoning systems. This enables the transformation of natural language data into machine-understandable knowledge and access to hidden knowledge that can be further processed to generate hypotheses.
CARNAP is designed to help you control the literature and proceed to the next step in your studies. We offer a software platform that allows various queries on the semantic content of medical knowledge.
It transforms natural language into our semantic model for knowledge infrastructure processing.
It applies our unimorphic mapping algorithm to identify relevant domain-specific entities and relations.
It reveals hidden knowledge, based on an innovative semantic validation framework.