Abstract
Library organizations have enthusiastically undertaken semantic web initiatives and in particular the data publishing as linked data. Nevertheless, different surveys report the experimental nature of initiatives and the consumer difficulty in re-using data. These barriers are a hindrance for using linked datasets, as an infrastructure that enhances the library and related information services. This paper presents an approach for encoding, as a Linked Vocabulary, the “tacit” knowledge of the information system that manages the data source. The objective is the improvement of the interpretation process of the linked data meaning of published datasets. We analyzed a digital library system, as a case study, for prototyping the “semantic data management” method, where data and its knowledge are natively managed, taking into account the linked data pillars. The ultimate objective of the semantic data management is to curate the correct consumers’ interpretation of data, and to facilitate the proper re-use. The prototype defines the ontological entities representing the knowledge, of the digital library system, that is not stored in the data source, nor in the existing ontologies related to the system’s semantics. Thus we present the local ontology and its matching with existing ontologies, Preservation Metadata Implementation Strategies (PREMIS) and Metadata Objects Description Schema (MODS), and we discuss linked data triples prototyped from the legacy relational database, by using the local ontology. We show how the semantic data management, can deal with the inconsistency of system data, and we conclude that a specific change in the system developer mindset, it is necessary for extracting and “codifying” the tacit knowledge, which is necessary to improve the data interpretation process.
Highlights
This paper is an extension of the paper [1] presented at the REMS 2018 Multidisciplinary Symposium on Computer Science and ICT in Stavropol, Russia, at the North–Caucasian Federal University.The ontologies used for exhibiting the linked dataset were already presented in the conference version of the paper
We present our approach for generating linked data (LD) from the relational database of a digital library (DigLib) system case study, and we show how we have addressed the capture of the data context, according to the Linked Data Best Practices, published by the World Wide Web Consortium [5] and related glossary [6]
Even semantically defined by existing ontologies regarding to well known DigLib metadata standards, data is managed by relying on a “tacit“ [9] ORG knowledge
Summary
This paper is an extension of the paper [1] presented at the REMS 2018 Multidisciplinary Symposium on Computer Science and ICT in Stavropol, Russia, at the North–Caucasian Federal University. We present our approach for generating LD from the relational database of a DigLib system case study, and we show how we have addressed the capture of the data context, according to the Linked Data Best Practices, published by the World Wide Web Consortium [5] and related glossary [6]. Even semantically defined by existing ontologies regarding to well known DigLib metadata standards (adopetd by the system), data is managed by relying on a “tacit“ [9] ORG knowledge. This knowledge should be made explicit, as an ontology, in order to support the data interpretation of consumers, and to improve the conveyance of the data meaning.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
