Attention-Survival Score: A Metric to Choose Better Keywords and Improve Visibility of Information.

In this paper, we propose a method to aid authors in choosing alternative keywords that help their papers gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, simultaneously, be keywords with fewer competitors. The competitors are derived from other papers containing the same keywords. Having fewer competitors would allow an author's paper to have a higher consult frequency. In order to recommend keywords, we must first determine an attention-survival score. The attention score is obtained using the popularity of a keyword. The survival score is derived from the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high attention-survival score. We used ontologies to ensure that alternative keywords proposed by our method are semantically related to the original authors' keywords that they wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative and input keywords. To test the sensibility of the ontology, we used two sources: WordNet and the Computer Science Ontology (CSO). Finally, we launched a survey for the human validation of our algorithm using keywords from Web of Science papers and three ontologies: WordNet, CSO, and DBpedia. We obtained good results from all our tests.