Feasibility Study for Procedural Knowledge Extraction in Biomedical Documents

Abstract

We propose how to extract procedural knowledge rather than declarative knowledge utilizing machine learning method with deep language processing features in scientific documents, as well as how to model it. We show the representation of procedural knowledge in PubMed abstracts and provide experiments that are quite promising in that it shows 82%, 63%, 73%, and 70% performances of purpose/solutions (two components of procedural knowledge model) extraction, process’s entity identification, entity association, and relation identification between processes respectively, even though we applied strict guidelines in evaluating the performance.