Fake news detector in the medical domain by reasoning with description logics

We investigate how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones. The not-trusted information comes in natural language (e.g. Depression is not an illness). To automatically convert into DLs, we used the FRED converter. Reasoning in DLs is then performed with the Racer and Hermit reasoners to detect inconsistencies. Each inconsistency signals to a possible medical myth. The detected inconsistencies are verbalised to the human agent, aiming to explain why a sentence is considered a medical myth. Our fake news detector is exemplified on in the psychocognitive and epidemiological fields.