Towards shared hypothesis testing in the biomedical domain

Evidence-based hypothesis testing assumes the existence of a causal chain between the facts. By studying the propagation of evidenced facts in the causal chain (hypothesis) we gain new insights on the progression of a disease. In practice, a hypothesis cannot always be substantiated with a complete asserted knowledge (inability to collect the required evidence), yet it is possible to test a hypothesis with missing knowledge with a lower confidence. In this work we propose a method to perform evidence-based hypothesis testing in the biomedical domain, such that specialists can evaluate confidence of their hypothesis and communicate their findings. We assume that a hypothesis is formalized in an OWL 2 EL ontology and the KB contains incomplete asserted knowledge (ABox). We extract a causal chain from an ontology and represent it as a DAG (node - fact, arc - causal relationship). Users assign importance weights to the facts which they think are more important to support the hypothesis. Evaluation of the hypothesis confidence is then done by computing a weighted sum of fact confidences over the directed path in the DAG (corresponding to the causal chain).