ERRT: Kenney Guo seminar

At this meeting of the eResearch Roundtable, doctoral candidate Kenney Guo will lead a session based on his upcoming presentation for the Doctoral Consortium of the 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW2016), titled "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, creating a systematic review is very time and resource consuming. And most importantly, conclusions from a systematic review may go out of date quickly. While technologies (including text mining and machine learning) have started to be applied to facilitate the manual process of creating a systematic review (e.g. article screening, data extraction, etc.), so far there is little work on automatically detecting signals to update a systematic review. This is a challenging task because a) interpreting the conclusion of a systematic review is challenging and b) detecting new studies that may possibly change the conclusion of a systematic review is also difficult. A promising approach is to make semantic representation of the claims made in both the systematic review and the primary studies it synthesizes. In this presentation, I will introduce a taxonomy to represent knowledge claims both in systematic reviews and primary studies with the goal of automatically updating the conclusion of a systematic review. I will report initial results on developing such a taxonomy and also describe possible machine learning models to automatically extract the claim information for each step of the process. 

Kenney (Jinlong) Guo's doctoral research focuses on using text mining and semantic technology to synthesize evidence from scientific literature.