Authors Identification within the Subject Area in the Semantic Library

Main Article Content

Abstract

The peculiarities of the task of authors identifying and determining author's contribution to publications in digital bibliographic codes are considered. The features of the problem of insufficient identification are manifested in the repetition of information, doubling, the presence of authors with completely coincidental names, self-quotation, autoplagiate and plagiarism itself. It is proposed to use publication information that has already been accumulated in the digital library in the form of related object area data and a variety of target thesaurus data, as the author and user of the library. This information contains links whereby keyword contexts, multiple co-authors, and term associations in dictionaries and thesauruses can be used to identify authorship. It is important that an array of scientific publications is considered, since they have an established traditional structure, which allows comparing fixed text elements (annotations, keywords, classifier codes, etc.). Thus, even if the names in the publications are fully matched, the question of authorship can be raised if the publications in the digital library correspond to different subject areas. Resolution of such contradictions is accomplished by evaluating a plurality of links of all elements of secondary publication information. The result of the comparison could be the addition of the author to a specific area, i.e. the extension of the addressee's thesaurus and the author's personal thesaurus, or the appearance of full namesakes in the library, but from different areas of knowledge. It has been shown that modern data analysis tools allow you to evaluate the author's contribution to publication, despite the fact that of course, only the scientific community can evaluate the real contribution to scientific research.

Article Details

References

1. Krämer T., Momeni F., Mayr P. Coverage of Author Identifiers in Web of Science and Scopus. – arXiv preprint arXiv:1703.01319, 2017 – arxiv.org.
2. Clement T.P. Authorship Matrix: A Rational Approach to Quantify Individual Contributions and Responsibilities in Multi-Author Scientific Articles // Science and Engineering Ethics. 2014. V. 20. P. 345–361.
https://doi.org/10.1007/s11948-013-9454-3.
3. Frische S. It is time for full disclosure of author contributions// Nature. 2012. P. 489.
URL: http://www.nature.com/news/it-is-time-for-full-disclosure-of-author-contributions-1.11475.3.
4. Cozzarelli N.R. Responsible authorship of papers in PNAS // Proceedings of the National Academy of Sciences of the United States of America. 2004. V. 101, No. 29. P. 10495.
5. URL: http://www.loc.gov/marc/marcdocz.html.
6. Шрейдер Ю.А. Тезаурусы в информатике и теоретической семантике // Научно-техническая информация. Сер. 2. 1971. № З. С. 21–24.
7. Гаврилова Т.А., Хорошевский В.Ф. Базы знаний интеллектуальных систем. СПб.: Питер, 2000. 384 с.
8. Лукашевич Н.В. Тезаурусы в задачах информационного поиска. М.: Изд-во МГУ, 2011. 495 с.
9. Муромский А.А., Тучкова Н.П. Об онтологии адресата в математической предметной области // Электронные библиотеки. 2018. Т. 21, № 6. С. 506–533.
10. Борисов Л.А., Орлов Ю.Н., Осминин К.П. Идентификация автора текста по распределению частот буквосочетаний // Препринты ИПМ им. М.В. Келдыша. 2013. № 27. 26 с.
URL: http://library.keldysh.ru/preprint.asp?id=2013-27.
11. URL: http://neon.niederlandistik.fu-berlin.de/textstat/.
12. Mohsen A.M., El-Makky N.M., Ghanem N. Author Identification Using Deep Learning, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, 2016. P. 898–903.
URL: https://doi.org/10.1109/ICMLA.2016.0161.
13. Маннинг К.Д., Рагхаван П., Шютце Х. Введение в информационный поиск. 2011.
14. Mikolov T., Chen K., Corrado G., Dean J. Efficient Estimation of Word Representations in Vector Space // Proceedings of Workshop at ICLR, 2013.
15. Mikolov T., Yih W.T., Zweig C. Linguistic Regularities in Continuous Space Word Representations // Proceedings of NAACL HLT, 2013.
16. Le Q., Mikolov T. Distributed Representations of Sentences and Documents // International Conference on Machine Learning, 2014. P. 1188–1196.
17. Strange K. Authorship: Why not just toss a coin? // American Journal of Physiology-Cell Physiology. 2008. V. 295, No. 3. P. 567–575. URL: https://doi.org/10.1152/ajpcell.00208.2008.
18. Meli D.B. Equivalence and Priority: Newton versus Leibniz: Including Leibniz's Unpublished Manuscripts on the Principia. Clarendon Press, 1993. P. 318.