Abstract
Knowledge graphs (KGs) are gaining prominence for their efficacy in data integration and knowledge representation within Current Research Information Systems (CRIS) Systems. By employing a semantic data model to represent entities, attributes, and their relationships, they prove versatile across scientific applications. In the era of Science platformization, particularly within CRIS systems, KGs serve to amalgamate diverse data sources and formats, facilitating the creation of interconnected data models. This enables stakeholders to access comprehensive, consistent information pertinent to their endeavors. This paper examines the pivotal roles of KGs in current and future data integration within CRIS systems, emphasizing their contributions to scientific reasoning. While their benefits include flexible knowledge modeling, support for semantic queries, and interoperability with various data sources, they face systemic limitations, particularly in methodological and technological aspects, hindering classical scientific investigations. The paper underscores the necessity for novel approaches to address these limitations, offering insights, use cases, and best practices for implementing KGs in CRIS systems. This paves the way for research institutions and scientific organizations to enhance their data analytics capabilities and support scientific reasoning effectively.
Published Version
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