A Study on the Corresponding between Schema.org’s Item Types with MARC Genre Terms List

Document Type : مقالات پژوهشی


1 PhD in Knowledge and Information Science; Science and Research Branch; Islamic Azad University;

2 Associate Professor; Faculty of Psychology and Educational Sciences; Allameh Tabataba’i University

3 Professor; Department of Communication and Knowledge Sciences; Science and Research Branch; Islamic Azad University

4 Associate Professor; Department of Communication and Knowledge Sciences; Science and Research Branch; Islamic Azad Universit

5 Associate Professor; Department of Communication and Knowledge Sciences; Science and Research Branch; Islamic Azad University


Introduction: The purpose of this study was to determine the degree of correspondence between data types of Schema.org and the representation of them in the MARC21 Genre Terms List.
Methodology: This research was an applied research based on a content analysis and the research population was a variety of data entities of the Schema.org. To collect data, a checklist-based tool on MARC Genre Terms List was used to check the degree of correspondence in Schema.org, and the structured observation method was used to initially identify each of the CreativeWork and then other entities in the Scheme.org were analyzed and the types of related data entities were identified
Findings: The findings of this study showed that there are 64 source-specific terms in the MARC that have no equivalent data entity in the Schema.org, and only 36% of the MARC Genre Terms List are consistent with the Schema.org. This means that for the 100 MARC Genre Terms List, there were only 36 equivalents in the Schema.org. Twenty-four terms of the MARC Genre Terms List were consistent accurately with the CreativeWork in Schema.org, and 12 were consistent with its other data entities.
Conclusion: To describe and organize digital objects, both properties must be taken into account because most data entities in the web are digitized versions of physical content objects and also considering that Schema.org does not consider some of the data entities in the bibliographic context, it must design the schema to describe all terms of a particular type of cultural heritage context (including the bibliographic context) for removing knowledge gap. It is recommended that specialized refinements and vocabularies of other sections of the cultural heritage (museum and archive context) be adapted to the types of the Schema.org to provide a thorough representation of knowledge in the context of cultural heritage.


آقاده، سمیرا (1397).طراحی طرح‌واره داده‌های مستند مبتنی بر روش داده‌های خرد و بررسی واکنش موتورهای کاوش وب به پیشینه‌های مبتنی بر آن. پایان‌نامه کارشناسی ارشد. دانشکده روانشناسی و علوم تربیتی. دانشگاه علامه طباطبائی.
محمدی استانی، مرتضی (1398). طراحی طرح‌واره قالب داده‌های خرد نسخه‌های خطی اسلامی-ایرانی و واکنش موتورهای کاوش وب به پیشینه‌های مبتنی بر آن. رساله دکتری. دانشکده روانشناسی و علوم تربیتی.دانشگاهاصفهان.
Aldaej, A. A. (2015). An enhanced semantic VLE based on schema. org and social media (Doctoral dissertation, University of Surrey). From: https://epubs.surrey.ac.uk/807070/1/Aldaej%20PhD%20Thesis.pdf (accessed 28 March 2021)
Amit, S. 2012. Introducing the knowledge graph: Things, not stringsOfficial Blog (of Google). https://googleblog.blogspot.se/2012/05/introducing-knowledge-graph-things-not.html.)Accessed 15 May. 2018(
Google developers .2017.  Introduction to structured data. https://developers.google.com/search/docs/guides/intro-structured-data/.)Accessed 15 July. 2018(
Jangra, R. 2018. SchemaBibEx: An Initiative for Networked Bibliographic Resource Description. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3149541. )accessed 5 June. 2018(
Meusel, R., Bizer, C., & Paulheim, H. 2015. A web-scale study of the adoption and evolution of the schema. org vocabulary over time. In Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics (p. 15). ACM. From: https://doi.org/10.1145/2797115.2797124
Schema.org (2019). Getting started with schema.org using Microdata. Retrieved July 19, 2019 from: http://schema.org/docs/gs.html
Senior, A. (2018). Bringing It All Together: Mapping Continuing Resources Vocabularies for Linked Data Discovery. The Serials Librarian, 74(1-4), 94-101, from: https://doi.org/10.1080/0361526X.2018.1428463
Şimşek, U., Kärle, E., Holzknecht, O., & Fensel, D. (2017, June). Domain specific semantic validation of schema. org annotations. In International Andrei Ershov Memorial Conference on Perspectives of System Informatics (pp. 417-429). Springer, Cham, from: https://doi.org/10.1007/978-3-319-74313-4_31
Vizine-Goetz, D., Childress, E., & Houghton, A. (2005, September). Web services for genre vocabularies. In Proceedings of the International Conference on Dublin Core and Metadata Applications (DC 2005), Madrid, Spain (September 12-15 2005). https://dcpapers.dublincore.org/pubs/article/view/823/819