Abstract:
The increasing complexity and volume of scientific and technological data necessitate advanced tools for effective data-driven analysis. Knowledge Graphs, with their capacity to encapsulate complex relationships among interconnected entities, have emerged as pivotal structures for organizing this vast amount of information. They enable a deeper understanding and exploration of data across various domains, notably in science and technology where the rapid proliferation of research outputs presents both opportunities and challenges. This paper presents the ScienceON Knowledge Graph System, a comprehensive framework designed to address integral challenges of integrating and analyzing scientific and technological data. We have developed the comprehensive infrastructure of ScienceON, a data ecosystem that harmonizes a wide spectrum of scientific and technological information. This encompasses everything from national R&D projects, scholarly papers, and patents to reports, author profiles, organizational details, keywords, and thematic categories. Our approach significantly advances the field not only by streamlining the aggregation of data via an Extract, Transform, Load process but also by facilitating the creation of a sophisticated knowledge graph. This knowledge graph meticulously interlinks research data, incorporating extensive metadata to accurately reflect the complex web of relationships within the science and technology domains. Our contributions are threefold: Firstly, we detail the creation of the ScienceON data ecosystem, highlighting an automated pipeline that ensures ongoing updates and expansion of data. Secondly, we describe the design of the ScienceON Knowledge Graph, which provides a detailed and interconnected representation of scientific and technological data. Lastly, we explore the application of the ScienceON Knowledge Graph in conducting graph-related experiments and in developing user-centric applications, demonstrating its versatility and utility. By employing rigorous data curation practices and utilizing the Resource Description Framework for data representation, we ensure the high quality and accessibility of our dataset, positioning the ScienceON Knowledge Graph as a gold standard in the realm of science and technology knowledge management. This initiative not only augments data management practices but also fosters the development of innovative applications and services, enhancing access to and understanding of the vast landscape of science and technology.
