Design and implementation of an evolutional data collecting system for the atomic and molecular databases

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Atomic and molecular database is useful for wide variety of fields of basic science and industrial applications. According to the present procedure of database development, atomic and molecular data are mainly collected from scientific papers through laborious work of staff scientists in the data centers. In this study, we have investigated a new application of information technology for evolutional development of atomic and molecular database based on automatic collection of scientific papers through the internet. We have developed a machine learning software which can recognize the existence of atomic and molecular data in unknown articles, using the bibliographic database of electron excitation and ionization cross sections as training examples. In order to characterize the articles, we firstly use the frequency of terms in the training and test examples. Secondly, we use information on the atomic and molecular states and technical terms corresponding to the atomic and molecular physics, which is found to be useful for improving the accuracy of the recognition. We study methods to recognize expressions for atomic and ionic species, charge states, electron configuration, spectral terms in the electronic form of scientific papers. We investigate frequency of appearance of each expression in the papers from different fields of physic published in Phys. Rev. A to E journals. Expression for nuclear species and molecules are also recognized separately, which are found to be useful to distinguish papers for atomic physics from those for the nuclear physics as well as physics of the condensed matter. Combining with the internet search engines, present result will make one possible to collect not only atomic and molecular data from articles but broader scientific information over a wide range of research fields.