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

پژوهشنامه کتابداری و اطلاع رسانی

Article 11, Volume 10, Issue 2 - Serial Number 20, March 2021, Pages 229-247   

Document Type: Research Article

DOI: 10.22067/infosci.2021.24040.0

Authors

Mahsa Fardehosseini¹; Seyed Mahdi Taheri^{* 2}; Nadjla Hariri³; Fahimeh Babalhavaeji⁴; Fatemeh Nooshinfard⁵

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

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

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

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

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

Abstract

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.

Keywords

Bibliographic context; CreativeWorks; Cultural heritage context; MARC Genre Terms List; Schema.org

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