Linked data for libraries: Creating a global knowledge space, a systematic literature review

Abstract

The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services.

Person‐Oriented Ontologies Analysis for Digital Humanities Collections from a Metadata Crosswalk Perspective

“…Metadata crosswalk is a method for analyzing how to transfer metadata from source metadata schemas to target metadata schemas, that contains semantic mapping and metadata conversion (Xu, Hess, & Akerman, 2018). In the digital library and cultural heritage fields, many studies have adopted metadata crosswalk as an essential metadata mapping method to accomplish interoperability among different conceptual models (Barroso, Hartmann, & Ribeiro, 2015; Gaitanou, Andreou, Sicilia, & Garoufallou, 2022; Gaitanou, Bountouri, & Gergatsoulis, 2012). But identification problems in practical data are still remained if only use lexical metadata crosswalk due to lack of enough semantic explanation (Ryan, Zhang, Durward, Matusiak, & Organisciak, 2020), such as the recognition problem about different people in the same names (Katsurai & Ohmukai, 2019).…”