A computer-aided bibliometric system to generate core article ranked lists in interdisciplinary subjects
Due to the tremendous increase and variations in serial publications, the impact of every peer-reviewed paper on different subjects is varying continually. Domain experts or researchers want to keep track of those latest and highly cited peer-reviewed papers; however they are finding it difficult to update or collect their subject core paper lists regularly and accurately. The evaluation of serial papers for generating and ranking core paper lists on different subjects becomes a very challenging task for scholars or librarians. Therefore, a computer-aided bibliometric system (CABS) was developed to generate a core article ranked list automatically. Four indicators  subject reference cited counts, subject total cited counts, subject reference period impact and subject reference cited history  were proposed to generate a subject core article ranking list. Seven different subjects including E-commerce, Data Mining, Supply Chain, Image Processing, Enterprise Resource Planning, Microarray and Expert Systems were used as samples. The turning point (TP) was proposed to determine the core article area in the paper citation analysis. The TP patterns observed were that all TPs had the same rate for four different subjects. The usage of TP patterns can also be used to verify the experimental results. This study provides experimental evidence to disprove three myths. Myth 1: the top papers on a subject (for instance, the top 10 papers) were all submitted to (S)SCI journals. Myth 2: the highly cited papers (cited counts >4) on interdisciplinary subjects were almost submitted to (S)SCI journals. Myth 3: the articles published in the top journals on a subject would be highly cited.
bibliometrics citation analysis scientometrics paper ranking