Given the exponential growth of knowledge production and users' increasing need for precise retrieval of scientific information, traditional databases face challenges such as data heterogeneity, semantic mismatches, weak interoperability, and irrelevant results. Semantic Web offers a novel solution to enhance the performance of these databases by conceptually organizing data and enabling intelligent information retrieval.
This study aims to conduct a systematic meta-synthesis of published research on the application of Semantic Web technologies and architectures in scientific databases, to identify, classify, and synthesizing their core and subsidiary components.
The research used a qualitative meta-synthesis method, following the approach of Sandelowski and Barroso (2006). A systematic search of reputable databases, including Scopus, Web of Science, and Google Scholar, was conducted to identify studies on the application of the Semantic Web to information databases. After applying the inclusion and exclusion criteria, 16 studies were selected for analysis. Data analysis was carried out through initial coding, concept analysis and synthesis, and the extraction of main categories.
The findings led to the identification and classification of 105 main components concerning the application of the Semantic Web in scientific databases. These components were categorized into six layers of Semantic Web architecture and 11 main dimensions of Semantic Web technologies, including Knowledge Representation and Modeling, Ontology Engineering, Information Extraction and Natural Language Processing, among others. Furthermore, several research gaps were identified, including a scarcity of studies in semantic integration and the need to focus on technologies that facilitate the transition from the Semantic Web to Artificial Intelligence.