Extracting knowledge claims for automatic evidence synthesis using semantic technology

Abstract

Systematic review, a form of evidence synthesis that critically appraises existing studies on the same topic and synthesizes study results, helps reduce the evidence gap. However, keeping the systematic review up-to-date is a great challenge partly due to the difficulty in interpreting the conclusion of a systematic review. A promising approach to this challenge is to make semantic representation of the claims made in both the systematic review and the included studies it synthesizes so that it’s possible to automatically predict whether the conclusion of a systematic review changes given a new study. In this dissertation work, we developed a taxonomy to represent knowledge claims both in systematic review and its included studies with the goal of automatically updating a systematic review. We then developed machine learning models to automatically predict a synthesized claim from claims in individual studies.