Learning Disease Causality Knowledge from Web of Health Data

Health information becomes importantly valuable to protect public health in the current coronavirus situation. Especially, knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will enable developing causality focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for doing inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies. The experimental work has processed 801 diseases in total from the UK NHS website linking with DBpedia datasets. As the result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.