Analyzing the Semantic Relatedness of Paper Abstracts: An Application to the Educational Research Field
Ionut Cristian Paraschiv, Mihai Dascălu, Ștefan Trăușan-Matu, Philippe Dessus · 2015
Each domain, along with its knowledge base, changes over time and every timeframe is centered on specific topics that emerge from different ongoing research projects. As searching for relevant resources is a time-consuming process, the automatic extraction of the most important and relevant articles from a domain becomes essential in supporting researchers in their day-to-day activities. The proposed analysis extends other previous researches focused on extracting co-citations between the papers, with the purpose of comparing their overall importance within the domain from a semantic perspective. Our method focuses on the semantic analysis of paper abstracts by using Natural Language Processing (NLP) techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or specific ontology distances, i.e., Word Net. Moreover, the defined mechanisms are enforced on two different sub domains from the corpora generated around the keywords "e-learning" and "computer". Graph visual representations are used to highlight the keywords of each sub domain, links among concepts and between articles, as well as specific document similarity views, or scores reflecting the keyword-abstract overlaps. In the end, conclusions and future improvements are presented, emphasizing nevertheless the key elements of our research support framework.