Intelligent Web Technologies for Data-Driven Scientific Research
AUTHORS
Adrian Lim Wei Ming,Department of Computer Science, National University of Singapore (NUS), Singapore
ABSTRACT
The growing scale and complexity of research data have changed how scientists and institutions conduct studies and share findings. Traditional manual approaches are no longer sufficient to handle the speed and volume of data produced today. This paper explores how intelligent web technologies—such as Artificial Intelligence (AI), semantic web systems, and data analytics—support data-driven research in improving efficiency, accuracy, and collaboration. By integrating intelligent systems with digital research infrastructures, scientists can automate data management, streamline analysis, and gain deeper insights from large datasets. The study reviews recent developments and applications that highlight how these technologies strengthen decision-making and cross-disciplinary collaboration. Findings show that intelligent web solutions enhance the accessibility, transparency, and reproducibility of research outcomes. At the same time, they raise important ethical, data privacy, and system interoperability considerations. This paper proposes a practical framework for applying intelligent web technologies to scientific research, with a focus on scalability and responsible innovation. The discussion aims to guide policymakers, research institutions, and technology developers in supporting Singapore's and the global community's transition towards a smarter and more data-driven research ecosystem.
KEYWORDS
Intelligent web technologies, Data-driven research, Artificial intelligence, Semantic web, Scientific collaboration, Digital innovation
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