Abstract-Background: a systematic review identifies, evaluates and synthesizes the available literature on a given topic using scientific and repeatable methodologies. The significant workload required and the subjectivity bias could affect results. Aim: semi-automate the selection process to reduce the amount of manual work needed and the consequent subjectivity bias. Method: extend and enrich the selection of primary studies using the existing technologies in the field of Linked Data and text mining. We define formally the selection process and we also develop a prototype that implements it. Finally, we conduct a case study that simulates the selection process of a systematic literature published in literature. Results: the process presented in this paper could reduce the work load of 20% with respect to the work load needed in the fully manually selection, with a recall of 100%. Conclusions: the extraction of knowledge from scientific studies through Linked Data and text mining techniques could be used in the selection phase of the systematic review process to reduce the work load and subjectivity bias.
In another study REF , Linked Data, Text Mining and Naïve Bayes techniques were used in an iterative and supervised process used in the Secondary Selection to reduce manual work and the bias of subjectivity.
Linked data approach for selection process automation in systematic reviews
15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011)
15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011)
Computer Science