We present the process and methods applied in undertaking the Traceability Challenge in addressing the Ubiquitous Grand Challenge, Research Project 3. Terms contained within queries (along with document collection terms, hence the “+”) have been enhanced to include semantic tags that indicate whether a term represents an action or an agent. This information is obtained by calling the Senna semantic role labeling tool. The standard TF-IDF component in TraceLab is then used to recover trace links. The QuEST method was applied to four datasets. Results based on the provided answer sets show that QuEST improved Mean Average Precision (MAP) for two artifact pairs of two of the datasets when the artifacts used natural language, but generally did not outperform unaugmented datasets not using natural language. We provide insights on this finding.