Editing of Digital Library Metadata Presented as Graphs
Abstract
The report is aimed at improving the software systems of general-purpose graph editors for the needs of object search and administration of the metadata network of the electronic library. One of the trends in the development of electronic libraries is the enrichment and complication of metadata due to the content of electronic documents stored in the library. Metadata helps to make better use of library collections, as it reflects the relevant areas of knowledge in a structural form, describing the context, content and structure of an electronic document throughout its life cycle. The idea of analyzing the structure of scientific discourse in order to display it in metadata dates back to the last years of the twentieth century, when the theory of structural rhetoric appeared, which continued its development in the first decade of the new century. The idea of modeling scientific and technological progress based on a network of problems dates back to the same years. In such a system, the user sees on the computer screen a complex network consisting of a network of scientific and technical problems, a network of innovative cycles of technical products related to the problems shown, and a network of library documents. Problem networks are visualized by a representative of a special class of applied software systems – a graph editor that reads a network representation in text or binary format from a file. The graph editor builds network elements in its memory in the form of objects and shows them in its windows in two forms: in the form of a marked-up colored graph and in the form of a table displaying the attributes of objects. The urgency of the problem of choosing a basic software tool and its subsequent modification for working with metadata networks of electronic libraries is revealed. The main provisions of the modification of the editor interface are presented, which allow adapting the graph editor software system to work with the metadata network of the electronic library. The proposed concept of the editor interface allows you to use an extended instance of a special representative of a class of similar software systems for the needs of an electronic library.
References
2. Ferro N., Silvello G. NESTOR: A formal model for digital archives. Information Processing & Management. 2013;49(6):1206-1240. doi: https://doi.org/10.1016/j.ipm.2013.05.001
3. Färber M. The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data. In: Ghidini C., Hartig O., et al. (eds.) The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science. Vol. 11779. Cham: Springer; 2019. p. 113-129. doi: https://doi.org/10.1007/978-3-030-30796-7_8
4. Gagarin A.P., Filimonov I.A. Problem Network as Access Gate into a Document Repository. Modern Information Technologies and IT-Education. 2020;16(3):582-597. (In Russ., abstract in Eng.) doi: https://doi.org/10.25559/SITITO.16.202003.582-597
5. Filimonov I. Experience of developing personal bibliographic retrieval system, oriented on specific area of scientific or engineering knowledge. Trudy MAI. 2020;(114):15. (In Russ., abstract in Eng.) doi: https://doi.org/10.34759/trd-2020-114-16
6. Devezas J. Graph-based entity-oriented search. ACM SIGIR Forum. 2021;55(1):15. doi: https://doi.org/10.1145/3476415.3476430
7. Wu J., Williams K.M., Chen H.-H., Khabsa M., Caragea C., Tuarob S., Ororbia A.G., Jordan D., Mitra P., Giles C.L. CiteSeerX: AI in a Digital Library Search Engine. AI Magazine. 2015;36(3):35-48. doi: https://doi.org/10.1609/aimag.v36i3.2601
8. Balog K. Entity-Oriented Search. The Information Retrieval Series. Vol. 39. Cham: Springer; 2018. 351 p. doi: https://doi.org/10.1007/978-3-319-93935-3
9. Siew C.S.Q., Wulff D.U., Beckage N.M., Kenett Y.N. Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics. Complexity. 2019;2019:2108423. doi: https://doi.org/10.1155/2019/2108423
10. Demchak B., Otasek D., Pico A.R., Bader G.D., Ono K., Settle B., Sage E., Morris J.H., Longabaugh W., Lopes C., Kucera M.,Treister A., Schwikowski B., Molenaar P., Ideker T. The Cytoscape Automation app article collection. F1000 Research. 2018;7:800. doi: https://doi.org/10.12688/f1000research.15355.1
11. Gagarin A.P., Filimonov I.A. Enriched Problem Network as a Core of the Metadata in a Digital Library. Modern Information Technologies and IT-Education. 2021;17(4):860-870. (In Russ., abstract in Eng.) doi: https://doi.org/10.25559/SITITO.17.202104.860-870
12. Constantin A., Peroni S., Pettifer S., Shotton D., Vitali F. The Document Components Ontology (Do-CO). Semantic Web. 2016;7(2):167-181. doi: https://doi.org/10.3233/SW-150177
13. Brandes U., Eiglsperger M., Lerner J., Pich C. Graph Markup Language (GraphML). In: Tamassia R. (ed.) Handbook of Graph Drawing and Visualization. 1st. ed. New York: Chapman and Hall/CRC; 2013. p. 517-541. doi: https://doi.org/10.1201/b15385-19
14. Bokhare A., Metkewar P.S. Visualization and Interpretation of Gephi and Tableau: A Comparative Study. In: Sengodan T., Murugappan M., Misra S. (eds.) Advances in Electrical and Computer Technologies. ICAECT 2020. Lecture Notes in Electrical Engineering. Vol. 711. Singapore: Springer; 2021. p. 11-23. doi: https://doi.org/10.1007/978-981-15-9019-1_2
15. Otasek D., Morris J.H., Bouças J., Pico A.R., Demchak B. Cytoscape Automation: empowering workflow-based network analysis. Genome Biology. 2019;20(1):185. doi: https://doi.org/10.1186/s13059-019-1758-4
16. Smoot M.E., Ono K., Ruscheinski J., Wang P.-L., Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431-432. doi: https://doi.org/10.1093/bioinformatics/btq675
17. Mouden Z.A.E., Jakimi A., Hajar M. Algorithm of Conversion Between Relational Data and Graph Schema. In: Rocha Á., Serrhini M. (eds.) Information Systems and Technologies to Support Learning. EMENA-ISTL 2018. Smart Innovation, Systems and Technologies. Vol. 111. Cham: Springer; 2019. p. 594-602. doi: https://doi.org/10.1007/978-3-030-03577-8_65
18. Zhu G., Iglesias C.A. Sematch: Semantic Entity Search from Knowledge Graph. CEUR Workshop Proceedings. 2016;1556:2. Available at: https://ceur-ws.org/Vol-1556/paper2.pdf (accessed 10.08.2022).
19. Pavanatto L. Designing Augmented Reality Virtual Displays for Productivity Work. In: 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). Bari, Italy: IEEE Computer Society; 2021. p. 459-460. doi: https://doi.org/10.1109/ISMAR-Adjunct54149.2021.00107
20. Jokinen J.P.P., Oulasvirta A., Howes A. Cognitive Modelling: From GOMS to Deep Reinforcement Learning. In: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA '22). New York, NY, USA: Association for Computing Machinery; 2022. Article number: 121. p. 1-3. doi: https://doi.org/10.1145/3491101.3503771
21. Khaet F., Alfimtsev A. The extended model of goals, operators, methods and selection rules (GOMS) for gesture interfaces. In: Proceedings of the 13th Central & Eastern European Software Engineering Conference in Russia (CEE-SECR '17). New York, NY, USA: Association for Computing Machinery; 2017. Article number: 8. doi: https://doi.org/10.1145/3166094.3166102
22. Beckert B., Beuster G. A Method for Formalizing, Analyzing, and Verifying Secure User Interfaces. In: Liu Z., He J. (eds.) Formal Methods and Software Engineering. ICFEM 2006. Lecture Notes in Computer Science. Vol. 4260. Berlin, Heidelberg: Springer; 2006. p. 55-73. doi: https://doi.org/10.1007/11901433_4
23. Kalpanadevi D. Building an Optimal Model of Cognitive Using KLM and Complexity Theory in Human Computer Interface. In: 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA). Coimbatore, India: IEEE Computer Society; 2021. p. 896-903. doi: https://doi.org/10.1109/ICECA52323.2021.9675914
24. Mishra W., Chowdhury A., Dhar D. Optimizing Operation Research Strategy for Design Intervention: A Framework for GOMS Selection Rule. In: Chakrabarti A., Chakrabarti D. (eds.) Research into Design for Communities. Vol. 1. ICoRD 2017. Smart Innovation, Systems and Technologies. Vol. 65. Singapore: Springer; 2017. p. 61-70. doi: https://doi.org/10.1007/978-981-10-3518-0_6
25. Filimonov I.A. Automation of a Network of Problems Using Programming Tools. Herald of Computer and Information Technologies. 2022;19(11):52-65. (In Russ., abstract in Eng.) doi: https://doi.org/10.14489/vkit.2022.11.pp.052-065
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication policy of the journal is based on traditional ethical principles of the Russian scientific periodicals and is built in terms of ethical norms of editors and publishers work stated in Code of Conduct and Best Practice Guidelines for Journal Editors and Code of Conduct for Journal Publishers, developed by the Committee on Publication Ethics (COPE). In the course of publishing editorial board of the journal is led by international rules for copyright protection, statutory regulations of the Russian Federation as well as international standards of publishing.
Authors publishing articles in this journal agree to the following: They retain copyright and grant the journal right of first publication of the work, which is automatically licensed under the Creative Commons Attribution License (CC BY license). Users can use, reuse and build upon the material published in this journal provided that such uses are fully attributed.
