CriteriaChecker: a knowledge graph approach to enhance integrity and ethics in academic publication

Academic writing is an integral part of scientific communities. This is a formal style of writing used by researchers and scholars to communicate critical analysis and evidence based arguments. This work showcased a graph-based approach for scraping, extracting, representing and evaluating the available academic writing forgery detection criteria and further enhancing the model by proposing a set of new age criteria. The proposed work is based on knowledge graphs and graph analytics capable of selecting subset of 16 criteria from the available superset of a cent of criterias provided by Bealls, Cabells, Shreshtha, and Think.Check.Submit, Scopus, and other relevant authors. The process for detecting the influencial parameters consists of 04 phases: dataset preparation, knowledge graph representation and making inferences through graph analytics and evaluation of results. The experimental results are then compared to the retraction database that consisting of information about retracted articles. The work enables the construction of an experiential knowledge graph that effectively identifies influential criteria, enhancing this list by incorporating new age criteria into current influential set and concluding in result by successfully detecting the academic predatory behavior.