A Semantic Domain-Specific Framework to Assist Researchers in Screening Contents

Screening and selecting appropriate research documents out of enormous existing contents, has drawn a wide range of research activities these days. Some researchers focus on developing automatic content assessment systems, while the others propose and expand some semantic rules and structures to facilitate the assessment process. There exist various content assessment methods which usually consider at least one of syntactic, semantic and structural perspectives through information retrieval or machine learning algorithms. In this paper, a semantic domain-specific framework is presented to assist researchers in their screening, selecting and recommending activities. The proposed framework is equipped with the ontology of key segments to assess various parts of research content, as well as WordNet and domain ontology, to reinforce semantic rules. The proposed framework is examined on a dataset of contents, and is also compared to the experts’ assessment of the same research materials. The comparison results reveal that the proposed semantic researcher-assisting framework has been successful in almost 70% of cases.