caa a22 4500 445350210 CHVBK 20180317142841.0 cr unu---uuuuu 170323e20111101xx s 000 0 eng 10.1007/s11192-011-0486-7 doi (NATIONALLICENCE)springer-10.1007/s11192-011-0486-7 Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals [Elektronische Daten] [Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza] As they are used to evaluate the importance of research at different levels by funding agencies and promotion committees, bibliometric indices have received a lot of attention from the scientific community over the last few years. Many bibliometric indices have been developed in order to take into account aspects not previously covered. The result is that, nowadays, the scientific community faces the challenge of selecting which of this pool of indices meets the required quality standards. In view of the vast number of bibliometric indices, it is necessary to analyze how they relate to each other (irrelevant, dependent and so on). Our main purpose is to learn a Bayesian network model from data to analyze the relationships among bibliometric indices. The induced Bayesian network is then used to discover probabilistic conditional (in)dependencies among the indices and, also for probabilistic reasoning. We also run a case study of 14 well-known bibliometric indices on computer science and artificial intelligence journals. Akadémiai Kiadó, Budapest, Hungary, 2011 Bibliometric indices nationallicence Bayesian networks nationallicence Conditional dependencies and conditional independencies nationallicence Computer science and artificial intelligence nationallicence Ibáñez Alfonso Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Madrid, Spain aut Larrañaga Pedro Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Madrid, Spain aut Bielza Concha Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Madrid, Spain aut Scientometrics Springer Netherlands 89/2(2011-11-01), 523-551 0138-9130 89:2<523 2011 89 11192 https://doi.org/10.1007/s11192-011-0486-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11192-011-0486-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ibáñez Alfonso Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Madrid, Spain aut NATIONALLICENCE 700 1- Larrañaga Pedro Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Madrid, Spain aut NATIONALLICENCE 700 1- Bielza Concha Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Madrid, Spain aut NATIONALLICENCE 773 0- Scientometrics Springer Netherlands 89/2(2011-11-01), 523-551 0138-9130 89:2<523 2011 89 11192 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer