Research on Fine-Grained Linked Data Creation for Digital Library Resources

Abstract The purpose of this study was to explore the extent to which individual factors such as academic tasks, preference for print sources and information skills influence distance learners’ use of digital libraries in a teacher distance education programme at the University of Education, Winneba in Ghana. The population of the study consisted of 1,834 distance students and from a sample size of 641 distance learners spread across three distance study centers, namely, Winneba (247), Kumasi (276) and Asante-Mampong (118). A total of 453 (70.67 percent) distance learners, selected randomly, completed a 31-item questionnaire. In addition, 30 distance education tutors, four coordinators and four librarians were purposively selected to participate in the study. The findings showed that academic tasks of distance learners did not require them to use digital library resources, which resulted in low or no academic use of digital libraries. The findings also pointed to a high preference for print resources among distance learners which resulted in the low use of digital library resources. Finally, it was noted that even though most of the distance learners possessed basic ICT skills, this did not result in their of use of digital library resources. To improve digital library use in distance education, it is recommended that academic librarians seek and foster collaboration in teaching information literacy and library instruction in distance education, an approach which must be backed by a policy that mandates the use of (digital) library scholarly resources in teaching and learning in distance education. It is also recommended that the Centre for Distance Education and the University Library collaborate to offer training programmes in specific digital resources and general information literacy courses for all distance education stakeholders in the university to boost digital library use.

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Expressing the Tacit Knowledge of a Digital Library System as Linked Data

Computers ◽

10.3390/computers8020049 ◽

2019 ◽

Vol 8 (2) ◽

pp. 49 ◽

Cited By ~ 2

Author(s):

Angela Di Iorio ◽

Marco Schaerf

Keyword(s):

Data Management ◽

Tacit Knowledge ◽

Digital Library ◽

Linked Data ◽

Data Publishing ◽

Library System ◽

Semantic Data ◽

Digital Library System ◽

Interpretation Process ◽

Data Source

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.

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