Ontologies and Research Data: A Theoretical and Methodological Overview - Cardoso de Oliveira, Caliel; Bragato Barros, Thiago Henrique (The Canadian Journal of Information and Library Science / La Revue canadienne des sciences de l’information et de bibliothéconomie, 2024)
This study examines the relationship between ontologies and open research data within the framework of the Semantic Web. The objective is to investigate the interconnectivity of both subjects through scholarly works that offer enhancements to the Web ecosystem and scientific research processes. The paper reviews critical theoretical frameworks related to the Semantic Web and the significance of metadata within this model. It also delves into the function that ontologies can fulfill in the Semantic Web landscape. The paper provides a historical overview of the emergence of ontologies and definitions in both Computer Science and Information Science. It chronicles how theorists in Information Science have progressively embraced the concept of ontologies since the late 20th century and assesses the current scholarly consensus on the subject. The study also addresses the importance of open research data in modern science by doing a systematic literature review; this study sources relevant publications from the Web of Science and Scopus databases, with a temporal focus from 2000 to 2023. The findings offer a comprehensive analysis of existing literature that bridges the two domains above, aiding in the theoretical and methodological systematization of the subject matter. The discussion section elaborates on the findings, offering insights into the evolutionary trajectory of the subject matter. Emphasis is placed on the utility of ontologies as tools for the sustainable and effective utilization of research data, accentuating the value of such data as a basis for future scholarly work. In conclusion, we advocate for information science to take a leading role in initiatives that leverage ontological frameworks to manage specialized knowledge in research data sets effectively, ensuring that such data remains an asset for advancing scientific understanding.