IOS Press Semantic Enrichment for Recommendation of Primary Studies in a Systematic Literature Review

Abstract. A Systematic Literature Review (SLR) identifies, evaluates and synthesizes the literature available for a given topic. This generally requires a significant human workload and has subjectivity bias that could affect the results of such a review. Automated document classification can be a valuable tool for recommending the selection of primary studies. In this paper, we propose an automated pre-selection approach based on text mining and semantic enrichment techniques. Each document candidate is firstly processed by a named entity extractor. The DBpedia URIs coming from the entity linking process are used as external sources of information. Our system collects the bag of words of those sources and it adds them to the initial document. A Multinomial Naive Bayes classifier discriminates whether the enriched document belongs to the positive example set or not. We used an existing manually performed SLR as golden dataset. We trained our system with different configurations of relevant papers and we assessed the goodness of our approach using a statistical approach. Results show a reduction of the manual workload of 18 % that a human researcher has to spend. As baseline, we compared the enriched approach with one based on a normal Multinomial Naive Bayes classifier. The improvements range from 2.5 % to 5 % depending on the dimension of the trained model