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  3. Research Information Systems And Knowledge Graphs: A Review.
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  3. Research Information Systems And Knowledge Graphs: A Review.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Published on: June 13, 2025

Research information systems and knowledge graphs: a review.

Muhammad Haris1, Sören Auer1, Markus Stocker1

  • 1TIB-Leibniz Information Center for Science and Technology, Leibniz University Hannover, Hannover, Germany.

Frontiers in Research Metrics and Analytics
|May 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Managing digital research artifacts is crucial for scientific discovery. Research Information Systems (RISs) and Knowledge Graphs (KGs) are key to organizing and accessing these vital research assets.

Keywords:
interoperable information systemsknowledge graphsresearch data managementresearch information systemsscholarly communicationscholarly knowledge

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Area of Science:

  • Computer Science
  • Information Science
  • Data Science

Background:

  • Digital research artifacts are fundamental to scientific progress but face challenges in organization and long-term availability due to increasing complexity and volume.
  • Ensuring discoverability, accessibility, and usability of research artifacts is essential for scientific communities across diverse fields.
  • Research Information Systems (RISs) are critical for curating, managing, and publishing research artifacts and objects.

Purpose of the Study:

  • To review the current state of the art in research information systems.
  • To highlight the role of various infrastructures in managing research artifacts.
  • To provide an overview of systems that enable discoverability, metadata management, data access, and structured knowledge representation.

Main Methods:

  • Review of scholarly identifier systems for persistent identification and global discoverability.
  • Analysis of bibliographic databases for managing research artifact metadata.
  • Presentation of Research Data Management (RDM) services for publishing and accessing research data.
  • Comprehensive overview of domain-specific and domain-agnostic Knowledge Graphs (KGs) for structured scientific knowledge representation.

Main Results:

  • Infrastructures like scholarly identifiers, bibliographic databases, RDM services, and KGs are vital for effective research artifact management.
  • Persistent identifiers enhance global discoverability and citeability of research artifacts.
  • RDM services facilitate the publishing and accessibility of research data.
  • Knowledge Graphs offer structured representations of scientific knowledge across different domains.

Conclusions:

  • Effective management of digital research artifacts is paramount for advancing science.
  • Research Information Systems, encompassing identifiers, databases, RDM services, and KGs, are indispensable tools for this management.
  • These systems collectively ensure that research artifacts are discoverable, accessible, usable, and well-represented, supporting research-intensive activities.