A tool for recommending keywords with more live and more attention.

Abstract In this paper, we propose a method to help authors to choose alternative Keywords that help their papers to gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, at the same time, be keywords that have fewer competitors. The competitors would be derived from other papers containing the same keywords. Having fewer competitors would allow the 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 by using the popularity of a keyword. The survival score is derived by 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 in order to ensure that alternative keywords proposed by our method are semantically related to the original authors keywords that authors wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative keywords and the input keywords. To test the sensibility of the ontology, we used two sources: WordNet and The Computer Science Ontology. Finally, we launched a survey to have human validation for our algorithm, by using keywords from Web of Science papers and three ontologies: WordNet, CSO and DBpedia. We obtained good results in all our tests.