New LIS Technologies and Services in Biosciences Education
Authors: Papadakis, Ioannis 
Chaleplioglou, Artemis 
Papavlasopoulos, Sozon 
Issue Date: 25-Feb-2019
Conference: 10th Qualitative and Quantitative Methods in Libraries (QQML), 22-25 May 2018, Chania, Greece 
Journal: Qualitative and Quantitative Methods in Libraries 
Volume: 7
Issue: 2
Keywords: Semantic web, Ontologies, Linked data, E-learning
Abstract: 
The advances in data management technologies lead to the transformation of biosciences into Big Data disciplines. Traditional and digital biomedical libraries utilize modern tools to support both teaching and learning of biosciences in all levels of education, from primary school to doctoral educational environment. Herein, we will describe the Semantic web technologies and services in the setting of biological and educational linked data. In particular, we will discuss the different types of open access web data and the challenges of volume, variability and complexity in their analyses. Currently, the use of distinct ontologies for biosciences and education represents a major problem in biomedical teaching. Their compilation and assembly is a priority for integrated functionality. The accessibility needs and preferences of biomedical students differ between traditional and e-learning contexts, while different existing and experimental virtual learning solutions have been proposed. From the Semantic web point of view the information should be organized and structured to produce curated metadata, linking different data sets into aggregated semantic LIS services. Such systems will facilitate the rapid retrieval and validation of biological data for educational purposes, building e-textbooks from open resources and shortening the information from multiple resources towards knowledge discovery, available for all teachers, students, doctoral fellows and residents. However, the developing Semantic web services need continuously evaluation and monitoring, since drawbacks arise in data retrieval and result errors because of information import from external datasets. To overcome the limitations of intelligent processing, we should focus in the accuracy and expressiveness of an integrated biomedical education ontology.
ISSN: 2241-1925
URI: https://uniwacris.uniwa.gr/handle/3000/584
Type: Conference Paper
Department: Department of Archival, Library and Information Studies 
School: School of Administrative, Economics and Social Sciences 
Affiliation: University of West Attica (UNIWA) 
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